An experimental method to deal with continuous predictors and possible alternative functional relationships between them and the trait of interest. In this case, we are modeling vessel size in response to environmental temperature.

## Load in libraries
library(bayou)
Loading required package: ape
Loading required package: geiger

Attaching package: ‘geiger’

The following object is masked _by_ ‘.GlobalEnv’:

    mkn.lik

Loading required package: phytools
Loading required package: maps
Loading required package: coda

Attaching package: ‘bayou’

The following object is masked from ‘package:grDevices’:

    colorRamp
library(treeplyr)
Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


Attaching package: ‘treeplyr’

The following object is masked from ‘package:stats’:

    reorder
library(diversitree)

Attaching package: ‘diversitree’

The following object is masked from ‘package:dplyr’:

    combine

The following object is masked from ‘package:coda’:

    mcmc
library(optimx)
## Set working directory
setwd("~/repos/growthforms/analysis")
td <- readRDS("../output/cleandata/tdOUwie.rds")
td
$phy

Phylogenetic tree with 351 tips and 350 internal nodes.

Tip labels:
    Tsuga_canadensis, Abies_concolor, Pseudotsuga_menziesii, Picea_smithiana, Pinus_longaeva, Pinus_cembra, ...
Node labels:
    Spermatophyta, , Pinales, , , , ...

Rooted; includes branch lengths.

$dat

attr(,"class")
[1] "treedata" "list"    
attr(,"tip.label")
  [1] "Tsuga_canadensis"              "Abies_concolor"                "Pseudotsuga_menziesii"         "Picea_smithiana"              
  [5] "Pinus_longaeva"                "Pinus_cembra"                  "Pinus_wallichiana"             "Pinus_halepensis"             
  [9] "Pinus_roxburghii"              "Pinus_mugo"                    "Pinus_sylvestris"              "Pinus_contorta"               
 [13] "Pinus_maximinoi"               "Pinus_radiata"                 "Pinus_patula"                  "Thuja_occidentalis"           
 [17] "Juniperus_oxycedrus"           "Juniperus_communis"            "Prumnopitys_ferruginea"        "Prumnopitys_taxifolia"        
 [21] "Dacrycarpus_dacrydioides"      "Podocarpus_totara"             "Ascarina_lucida"               "Pseudowintera_colorata"       
 [25] "Hedycarya_arborea"             "Eusideroxylon_zwageri"         "Beilschmiedia_tawa"            "Laurus_azorica"               
 [29] "Laurus_nobilis"                "Litsea_calicaris"              "Nectandra_megapotamica"        "Ocotea_puberula"              
 [33] "Virola_surinamensis"           "Pycnanthus_angolensis"         "Magnolia_grandiflora"          "Magnolia_tripetala"           
 [37] "Magnolia_acuminata"            "Buxus_sempervirens"            "Platanus_orientalis"           "Knightia_excelsa"             
 [41] "Liquidambar_styraciflua"       "Stachyurus_himalaicus"         "Terminalia_amazonia"           "Terminalia_ivorensis"         
 [45] "Punica_granatum"               "Duabanga_moluccana"            "Sonneratia_alba"               "Neomyrtus_pedunculata"        
 [49] "Lophomyrtus_obcordata"         "Leptospermum_scoparium"        "Kunzea_ericoides"              "Eucalyptus_globoidea"         
 [53] "Eucalyptus_regnans"            "Eucalyptus_obliqua"            "Eucalyptus_sieberi"            "Eucalyptus_grandis"           
 [57] "Eucalyptus_globulus"           "Salvadora_persica"             "Capparis_spinosa"              "Thymelaea_hirsuta"            
 [61] "Shorea_leprosula"              "Shorea_ovata"                  "Guazuma_ulmifolia"             "Apeiba_tibourbou"             
 [65] "Apeiba_membranacea"            "Grewia_villosa"                "Sterculia_apetala"             "Cavanillesia_platanifolia"    
 [69] "Ceiba_pentandra"               "Spondias_mombin"               "Tapirira_guianensis"           "Anacardium_excelsum"          
 [73] "Mangifera_indica"              "Rhus_glabra"                   "Rhus_coriaria"                 "Pistacia_lentiscus"           
 [77] "Pistacia_atlantica"            "Billia_hippocastanum"          "Acer_cappadocicum"             "Harpullia_cupanioides"        
 [81] "Sapindus_saponaria"            "Allophylus_cobbe"              "Nephelium_lappaceum"           "Litchi_chinensis"             
 [85] "Alectryon_excelsus"            "Swietenia_macrophylla"         "Carapa_procera"                "Carapa_grandiflora"           
 [89] "Carapa_guianensis"             "Cedrela_odorata"               "Cabralea_canjerana"            "Simarouba_amara"              
 [93] "Bulnesia_arborea"              "Eucryphia_cordifolia"          "Eucryphia_lucida"              "Weinmannia_trichosperma"      
 [97] "Aristotelia_serrata"           "Elaeocarpus_hookerianus"       "Elaeocarpus_japonicus"         "Goupia_glabra"                
[101] "Caryocar_glabrum"              "Bischofia_javanica"            "Hieronyma_alchorneoides"       "Erythroxylum_confusum"        
[105] "Erythroxylum_macrophyllum"     "Erythroxylum_panamense"        "Mammea_africana"               "Calophyllum_brasiliense"      
[109] "Symphonia_globulifera"         "Drypetes_lateriflora"          "Salix_alba"                    "Salix_triandra"               
[113] "Salix_alaxensis"               "Pera_glabrata"                 "Hevea_brasiliensis"            "Gymnanthes_lucida"            
[117] "Hura_crepitans"                "Euphorbia_tirucalli"           "Nothofagus_menziesii"          "Nothofagus_fusca"             
[121] "Nothofagus_solandri"           "Nothofagus_dombeyi"            "Fagus_sylvatica"               "Quercus_semecarpifolia"       
[125] "Quercus_coccifera"             "Quercus_faginea"               "Castanopsis_indica"            "Quercus_ilex"                 
[129] "Myrica_esculenta"              "Juglans_regia"                 "Juglans_australis"             "Casuarina_equisetifolia"      
[133] "Casuarina_cunninghamiana"      "Carpinus_betulus"              "Betula_utilis"                 "Betula_nana"                  
[137] "Betula_pubescens"              "Betula_pendula"                "Krugiodendron_ferreum"         "Rhamnus_alaternus"            
[141] "Rhamnus_cathartica"            "Ziziphus_mistol"               "Ziziphus_mucronata"            "Ziziphus_jujuba"              
[145] "Ziziphus_lotus"                "Aphananthe_aspera"             "Gironniera_subaequalis"        "Trema_cannabina"              
[149] "Trema_tomentosa"               "Trema_orientalis"              "Milicia_excelsa"               "Morus_alba"                   
[153] "Morus_rubra"                   "Maclura_pomifera"              "Maclura_cochinchinensis"       "Maclura_tricuspidata"         
[157] "Broussonetia_papyrifera"       "Brosimum_lactescens"           "Brosimum_alicastrum"           "Brosimum_guianense"           
[161] "Brosimum_utile"                "Brosimum_rubescens"            "Antiaris_toxicaria"            "Maquira_calophylla"           
[165] "Pseudolmedia_laevigata"        "Perebea_guianensis"            "Ficus_carica"                  "Rubus_swinhoei"               
[169] "Rubus_idaeus"                  "Hagenia_abyssinica"            "Sarcopoterium_spinosum"        "Polylepis_australis"          
[173] "Rosa_canina"                   "Adenostoma_sparsifolium"       "Adenostoma_fasciculatum"       "Sorbaria_sorbifolia"          
[177] "Holodiscus_discolor"           "Spiraea_salicifolia"           "Lyonothamnus_floribundus"      "Oemleria_cerasiformis"        
[181] "Prunus_lusitanica"             "Prunus_laurocerasus"           "Prunus_serotina"               "Prunus_virginiana"            
[185] "Prunus_mahaleb"                "Prunus_spinulosa"              "Prunus_avium"                  "Prunus_davidiana"             
[189] "Prunus_persica"                "Prunus_spinosa"                "Prunus_cerasifera"             "Vauquelinia_californica"      
[193] "Sorbus_americana"              "Sorbus_aucuparia"              "Mespilus_germanica"            "Amelanchier_alnifolia"        
[197] "Photinia_villosa"              "Sorbus_aria"                   "Malus_pumila"                  "Malus_baccata"                
[201] "Photinia_davidiana"            "Pyrus_calleryana"              "Rhaphiolepis_indica"           "Eriobotrya_japonica"          
[205] "Apuleia_leiocarpa"             "Castanospermum_australe"       "Tetraberlinia_bifoliolata"     "Tamarindus_indica"            
[209] "Hymenaea_courbaril"            "Baikiaea_plurijuga"            "Caesalpinia_paraguariensis"    "Erythrophleum_ivorense"       
[213] "Prosopis_africana"             "Acacia_nilotica"               "Acacia_tortilis"               "Acacia_gerrardii"             
[217] "Acacia_karroo"                 "Acacia_polyacantha"            "Acacia_nigrescens"             "Acacia_erioloba"              
[221] "Cedrelinga_cateniformis"       "Albizia_procera"               "Pterocarpus_officinalis"       "Anagyris_foetida"             
[225] "Sophora_microphylla"           "Spartium_junceum"              "Lecointea_amazonica"           "Bocoa_prouacensis"            
[229] "Swartzia_polyphylla"           "Swartzia_simplex"              "Swartzia_arborescens"          "Swartzia_panacoco"            
[233] "Swartzia_cardiosperma"         "Minquartia_guianensis"         "Ochanostachys_amentacea"       "Alepis_flavida"               
[237] "Tamarix_aphylla"               "Salsola_vermiculata"           "Alangium_chinense"             "Alangium_villosum"            
[241] "Nyssa_sylvatica"               "Nyssa_aquatica"                "Norantea_guianensis"           "Ficalhoa_laurifolia"          
[245] "Eurya_japonica"                "Gordonia_lasianthus"           "Schima_wallichii"              "Camellia_japonica"            
[249] "Camellia_sinensis"             "Camellia_caudata"              "Camellia_brevistyla"           "Camellia_oleifera"            
[253] "Sideroxylon_obtusifolium"      "Manilkara_bidentata"           "Symplocos_paniculata"          "Symplocos_anomala"            
[257] "Symplocos_lucida"              "Symplocos_sumuntia"            "Symplocos_cochinchinensis"     "Symplocos_glomerata"          
[261] "Symplocos_tinctoria"           "Symplocos_martinicensis"       "Bertholletia_excelsa"          "Pterostyrax_hispidus"         
[265] "Halesia_carolina"              "Styrax_camporum"               "Styrax_officinalis"            "Arbutus_andrachne"            
[269] "Rhododendron_anthopogon"       "Dracophyllum_strictum"         "Sprengelia_incarnata"          "Trochocarpa_laurina"          
[273] "Monotoca_elliptica"            "Oxydendrum_arboreum"           "Lyonia_lucida"                 "Lyonia_ovalifolia"            
[277] "Pieris_formosa"                "Pieris_japonica"               "Gaultheria_shallon"            "Gaultheria_mucronata"         
[281] "Zenobia_pulverulenta"          "Andromeda_polifolia"           "Ilex_crenata"                  "Ilex_dumosa"                  
[285] "Ilex_decidua"                  "Ilex_macfadyenii"              "Ilex_opaca"                    "Ilex_aquifolium"              
[289] "Ilex_integra"                  "Ilex_latifolia"                "Ilex_canariensis"              "Ilex_coriacea"                
[293] "Ilex_glabra"                   "Ilex_chinensis"                "Ilex_theezans"                 "Ilex_guianensis"              
[297] "Ilex_anomala"                  "Ilex_micrococca"               "Ilex_rotunda"                  "Ilex_verticillata"            
[301] "Ilex_mitis"                    "Ilex_goshiensis"               "Sambucus_nigra"                "Sambucus_racemosa"            
[305] "Viburnum_dilatatum"            "Viburnum_opulus"               "Viburnum_tinus"                "Viburnum_odoratissimum"       
[309] "Lonicera_maackii"              "Lonicera_chrysantha"           "Pennantia_corymbosa"           "Aralidium_pinnatifidum"       
[313] "Griselinia_littoralis"         "Schefflera_morototoni"         "Hedera_helix"                  "Schefflera_volkensii"         
[317] "Schefflera_digitata"           "Carpodetus_serratus"           "Garrya_elliptica"              "Aucuba_japonica"              
[321] "Fagraea_fragrans"              "Aspidosperma_quebracho-blanco" "Alstonia_boonei"               "Alstonia_scholaris"           
[325] "Nerium_oleander"               "Uncaria_tomentosa"             "Vangueria_infausta"            "Genipa_americana"             
[329] "Nicotiana_glauca"              "Haenianthus_salicifolius"      "Olea_capensis"                 "Olea_europaea"                
[333] "Phillyrea_latifolia"           "Syringa_reticulata"            "Ligustrum_japonicum"           "Ligustrum_lucidum"            
[337] "Ligustrum_sinense"             "Jasminum_fruticans"            "Avicennia_marina"              "Trichanthera_gigantea"        
[341] "Paulownia_tomentosa"           "Jacaranda_copaia"              "Tecoma_stans"                  "Spathodea_campanulata"        
[345] "Newbouldia_laevis"             "Catalpa_bignonioides"          "Catalpa_speciosa"              "Mansoa_verrucifera"           
[349] "Tabebuia_rosea"                "Tectona_grandis"               "Vitex_agnus-castus"           
attr(,"dropped")
attr(,"dropped")$dropped_from_tree
[1] "Myrtus_communis"          "Nyctanthes_arbor-tristis" "Paliurus_spina-christi"   "Ruyschia_tremadena"      

attr(,"dropped")$dropped_from_data
   [1] "Blasia_pusilla"                "Lunularia_cruciata"            "Marchantia_polymorpha"         "Riccia_fluitans"              
   [5] "Reboulia_hemisphaerica"        "Marchantia_foliacea"           "Conocephalum_conicum"          "Haplomitrium_gibbsiae"        
   [9] "Fossombronia_pusilla"          "Pellia_endiviifolia"           "Pellia_epiphylla"              "Riccardia_multifida"          
  [13] "Riccardia_latifrons"           "Aneura_pinguis"                "Aneura_mirabilis"              "Metzgeria_conjugata"          
  [17] "Metzgeria_furcata"             "Metzgeria_pubescens"           "Metzgeria_temperata"           "Porella_pinnata"              
  [21] "Porella_cordaeana"             "Porella_platyphylla"           "Porella_obtusata"              "Porella_arboris"              
  [25] "Frullania_dilatata"            "Frullania_microphylla"         "Frullania_fragilifolia"        "Frullania_teneriffae"         
  [29] "Frullania_tamarisci"           "Radula_aquilegia"              "Radula_complanata"             "Jubula_hutchinsiae"           
  [33] "Lejeunea_lamacerina"           "Lejeunea_cavifolia"            "Blepharostoma_trichophyllum"   "Herbertus_borealis"           
  [37] "Lepidozia_reptans"             "Kurzia_trichoclados"           "Bazzania_tricrenata"           "Bazzania_trilobata"           
  [41] "Lophocolea_heterophylla"       "Lophocolea_bidentata"          "Chiloscyphus_pallescens"       "Pedinophyllum_interruptum"    
  [45] "Plagiochila_spinulosa"         "Plagiochila_punctata"          "Plagiochila_asplenioides"      "Plagiochila_porelloides"      
  [49] "Cephaloziella_divaricata"      "Odontoschisma_denudatum"       "Nowellia_curvifolia"           "Lophozia_ventricosa"          
  [53] "Diplophyllum_albicans"         "Diplophyllum_obtusifolium"     "Anastrophyllum_minutum"        "Jungermannia_exsertifolia"    
  [57] "Barbilophozia_barbata"         "Barbilophozia_hatcheri"        "Calypogeia_arguta"             "Harpanthus_flotovianus"       
  [61] "Harpanthus_scutatus"           "Jungermannia_obovata"          "Nardia_scalaris"               "Gymnomitrion_concinnatum"     
  [65] "Marsupella_emarginata"         "Geocalyx_graveolens"           "Jungermannia_pumila"           "Jungermannia_atrovirens"      
  [69] "Saccogyna_viticulosa"          "Calypogeia_fissa"              "Calypogeia_integristipula"     "Calypogeia_neesiana"          
  [73] "Polytrichum_commune"           "Sphagnum_contortum"            "Sphagnum_rubellum"             "Sphagnum_squarrosum"          
  [77] "Sphagnum_fuscum"               "Sphagnum_palustre"             "Sphagnum_recurvum"             "Sphagnum_fallax"              
  [81] "Sphagnum_cuspidatum"           "Entosthodon_laevis"            "Physcomitrella_patens"         "Calymperes_palisotii"         
  [85] "Goniomitrium_acuminatum"       "Dicranum_scoparium"            "Philonotis_fontana"            "Plagiothecium_curvifolium"    
  [89] "Sanionia_uncinata"             "Brachythecium_salebrosum"      "Brachythecium_acuminatum"      "Brachythecium_rutabulum"      
  [93] "Hylocomium_splendens"          "Pleurozium_schreberi"          "Hypnum_cupressiforme"          "Palustriella_falcata"         
  [97] "Anomodon_viticulosus"          "Jaegerina_scariosa"            "Hamatocaulis_vernicosus"       "Phaeoceros_laevis"            
 [101] "Anthoceros_formosae"           "Anthoceros_punctatus"          "Huperzia_phyllantha"           "Huperzia_drummondii"          
 [105] "Huperzia_australiana"          "Huperzia_selago"               "Huperzia_lucidula"             "Lycopodiella_inundata"        
 [109] "Lycopodiella_cernua"           "Lycopodium_casuarinoides"      "Lycopodium_clavatum"           "Lycopodium_deuterodensum"     
 [113] "Lycopodium_volubile"           "Austrolycopodium_fastigiatum"  "Diphasium_scariosum"           "Lycopodium_annotinum"         
 [117] "Lycopodium_obscurum"           "Diphasiastrum_complanatum"     "Diphasiastrum_alpinum"         "Isoetes_schweinfurthii"       
 [121] "Cephaloceraton_histrix"        "Isoetes_echinospora"           "Isoetes_lacustris"             "Selaginella_selaginoides"     
 [125] "Selaginella_kraussiana"        "Selaginella_remotifolia"       "Selaginella_pygmaea"           "Selaginella_uliginosa"        
 [129] "Selaginella_gracillima"        "Selaginella_delicatissima"     "Selaginella_helvetica"         "Selaginella_frondosa"         
 [133] "Selaginella_longipinna"        "Selaginella_moellendorffii"    "Selaginella_tamariscina"       "Selaginella_biformis"         
 [137] "Marattia_douglasii"            "Angiopteris_wangii"            "Angiopteris_hokouensis"        "Angiopteris_caudatiformis"    
 [141] "Angiopteris_evecta"            "Tmesipteris_truncata"          "Psilotum_nudum"                "Tmesipteris_tannensis"        
 [145] "Tmesipteris_obliqua"           "Tmesipteris_elongata"          "Botrychium_virginianum"        "Botrychium_lanuginosum"       
 [149] "Botrychium_multifidum"         "Botrychium_schaffneri"         "Botrychium_matricariifolium"   "Botrychium_lunaria"           
 [153] "Ophioglossum_nudicaule"        "Ophioglossum_costatum"         "Ophioglossum_pendulum"         "Ophioglossum_petiolatum"      
 [157] "Ophioglossum_lusitanicum"      "Ophioglossum_vulgatum"         "Ophioglossum_reticulatum"      "Equisetum_diffusum"           
 [161] "Equisetum_fluviatile"          "Equisetum_telmateia"           "Equisetum_palustre"            "Equisetum_sylvaticum"         
 [165] "Equisetum_pratense"            "Equisetum_arvense"             "Equisetum_scirpoides"          "Equisetum_variegatum"         
 [169] "Equisetum_ramosissimum"        "Equisetum_hyemale"             "Equisetum_laevigatum"          "Equisetum_giganteum"          
 [173] "Osmunda_claytoniana"           "Osmunda_vachellii"             "Osmunda_japonica"              "Osmunda_regalis"              
 [177] "Todea_barbara"                 "Leptopteris_fraseri"           "Leptopteris_hymenophylloides"  "Leptopteris_superba"          
 [181] "Dicranopteris_linearis"        "Dicranopteris_pedata"          "Dicranopteris_gigantea"        "Diplopterygium_glaucoides"    
 [185] "Gleichenia_japonica"           "Diplopterygium_glaucum"        "Sticherus_laevigatus"          "Sticherus_cunninghamii"       
 [189] "Gleichenia_dicarpa"            "Gleichenia_microphylla"        "Trichomanes_diversifrons"      "Trichomanes_pinnatum"         
 [193] "Cephalomanes_javanicum"        "Abrodictyum_rigidum"           "Trichomanes_borbonicum"        "Polyphlebium_venosum"         
 [197] "Didymoglossum_reptans"         "Didymoglossum_erosum"          "Crepidomanes_latealatum"       "Crepidomanes_auriculatum"     
 [201] "Vandenboschia_collariata"      "Hymenophyllum_pulcherrimum"    "Hymenophyllum_poolii"          "Hymenophyllum_flabellatum"    
 [205] "Hymenophyllum_scabrum"         "Hymenophyllum_villosum"        "Hymenophyllum_sanguinolentum"  "Hymenophyllum_dilatatum"      
 [209] "Hymenophyllum_demissum"        "Hymenophyllum_flexuosum"       "Hymenophyllum_australe"        "Hymenophyllum_minimum"        
 [213] "Hymenophyllum_darwinii"        "Hymenophyllum_polyanthos"      "Hymenophyllum_sibthorpioides"  "Hymenophyllum_tunbrigense"    
 [217] "Hymenophyllum_peltatum"        "Hymenophyllum_revolutum"       "Hymenophyllum_cupressiforme"   "Hymenophyllum_pumilum"        
 [221] "Schizaea_fistulosa"            "Anemia_phyllitidis"            "Schizaea_pectinata"            "Schizaea_dichotoma"           
 [225] "Lygodium_japonicum"            "Lygodium_circinatum"           "Lygodium_kerstenii"            "Cibotium_barometz"            
 [229] "Cibotium_chamissoi"            "Cibotium_glaucum"              "Calochlaena_dubia"             "Dicksonia_fibrosia"           
 [233] "Balantium_antarcticum"         "Dicksonia_baudouinii"          "Dicksonia_squarrosa"           "Cyathea_leichhardtiana"       
 [237] "Sphaeropteris_cooperi"         "Cyathea_contaminans"           "Sphaeropteris_brunoniana"      "Cyathea_alata"                
 [241] "Cyathea_karsteniana"           "Cyathea_senilis"               "Cyathea_spinulosa"             "Cyathea_australis"            
 [245] "Alsophila_cuspidata"           "Cyathea_colensoi"              "Salvinia_natans"               "Salvinia_adnata"              
 [249] "Azolla_nilotica"               "Azolla_pinnata"                "Azolla_filiculoides"           "Azolla_mexicana"              
 [253] "Azolla_microphylla"            "Azolla_caroliniana"            "Calamistrum_globuliferum"      "Pilularia_novae"              
 [257] "Marsilea_mutica"               "Marsilea_minuta"               "Marsilea_quadrifolia"          "Marsilea_drummondii"          
 [261] "Marsilea_capensis"             "Marsilea_coromandelina"        "Marsilea_villifolia"           "Marsilea_vera"                
 [265] "Marsilea_aegyptiaca"           "Marsilea_ephippiocarpa"        "Marsilea_farinosa"             "Marsilea_macrocarpa"          
 [269] "Saccoloma_elegans"             "Saccoloma_inaequale"           "Lindsaea_odorata"              "Lindsaea_javanensis"          
 [273] "Lindsaea_ensifolia"            "Cryptogramma_crispa"           "Pityrogramma_calomelanos"      "Anogramma_leptophylla"        
 [277] "Pteris_platyzomopsis"          "Pteris_vittata"                "Onychium_japonicum"            "Pteris_tremula"               
 [281] "Pteris_semipinnata"            "Pteris_setulosocostulata"      "Pteris_ensiformis"             "Pteris_excelsa"               
 [285] "Pteris_cretica"                "Pteris_multifida"              "Pteris_berteroana"             "Pteris_microptera"            
 [289] "Cheilanthes_lanosa"            "Pellaea_falcata"               "Pellaea_rotundifolia"          "Pellaea_boivinii"             
 [293] "Pellaea_dura"                  "Cheilanthes_argentea"          "Pellaea_calomelanos"           "Cheilanthes_multifida"        
 [297] "Cheilanthes_hirta"             "Cheilanthes_eckloniana"        "Cheilanthes_sieberi"           "Cheilanthes_lasiophylla"      
 [301] "Cheilanthes_distans"           "Anetium_citrifolium"           "Vittaria_isoetifolia"          "Vittaria_lineata"             
 [305] "Vittaria_stipitata"            "Hecistopteris_pumila"          "Haplopteris_volkensii"         "Vittaria_ensiformis"          
 [309] "Vittaria_flexuosa"             "Adiantum_capillus-veneris"     "Adiantum_raddianum"            "Adiantum_chilense"            
 [313] "Adiantum_hispidulum"           "Adiantum_diaphanum"            "Adiantum_pedatum"              "Adiantum_obliquum"            
 [317] "Adiantum_latifolium"           "Adiantum_flabellulatum"        "Adiantum_bonatianum"           "Adiantum_lunulatum"           
 [321] "Adiantum_caudatum"             "Adiantum_malesianum"           "Monachosorum_henryi"           "Pteridium_aquilinum"          
 [325] "Pteridium_esculentum"          "Histiopteris_incisa"           "Hypolepis_millefolium"         "Hypolepis_punctata"           
 [329] "Hypolepis_muelleri"            "Dennstaedtia_punctilobula"     "Dennstaedtia_scabra"           "Microlepia_strigosa"          
 [333] "Microlepia_platyphylla"        "Microlepia_speluncae"          "Microlepia_hookeriana"         "Microlepia_marginata"         
 [337] "Hymenasplenium_cardiophyllum"  "Hymenasplenium_hondoense"      "Asplenium_cheilosorum"         "Hymenasplenium_excisum"       
 [341] "Asplenium_filipes"             "Asplenium_unilaterale"         "Asplenium_juglandifolium"      "Ceterach_aurea"               
 [345] "Asplenium_ensiforme"           "Asplenium_cuneatiforme"        "Asplenium_caudatum"            "Asplenium_polyodon"           
 [349] "Asplenium_sphenotomum"         "Asplenium_affine"              "Asplenium_wilfordii"           "Asplenium_pseudowilfordii"    
 [353] "Asplenium_finlaysonianum"      "Asplenium_yoshinagae"          "Asplenium_nidus"               "Asplenium_setoi"              
 [357] "Cyclosorus_parasiticus"        "Asplenium_phyllitidis"         "Asplenium_antrophyoides"       "Asplenium_cymbifolium"        
 [361] "Asplenium_antiquum"            "Asplenium_anisophyllum"        "Asplenium_chathamense"         "Asplenium_lamprophyllum"      
 [365] "Asplenium_shuttleworthianum"   "Asplenium_dimorphum"           "Asplenium_difforme"            "Asplenium_hookerianum"        
 [369] "Asplenium_richardii"           "Asplenium_viellardii"          "Asplenium_oblongifolium"       "Asplenium_obliquum"           
 [373] "Asplenium_flaccidum"           "Asplenium_obtusatum"           "Asplenium_simplicifrons"       "Asplenium_prolongatum"        
 [377] "Asplenium_griffithianum"       "Asplenium_tenerum"             "Asplenium_rutifolium"          "Asplenium_theciferum"         
 [381] "Asplenium_loxoscaphoides"      "Asplenium_feei"                "Asplenium_elliottii"           "Asplenium_smedsii"            
 [385] "Asplenium_mannii"              "Asplenium_christii"            "Asplenium_sandersonii"         "Asplenium_dregeanum"          
 [389] "Asplenium_gemmiferum"          "Asplenium_lolegnamense"        "Asplenium_parvifolium"         "Asplenium_octoploideum"       
 [393] "Phyllitopsis_hybrida"          "Ceterach_officinarum"          "Asplenium_cyprium"             "Asplenium_hemionitis"         
 [397] "Asplenium_septentrionale"      "Asplenium_seelosii"            "Asplenium_davallioides"        "Asplenium_bulbiferum"         
 [401] "Asplenium_wrightii"            "Asplenium_wrightioides"        "Asplenium_variabile"           "Asplenium_protensum"          
 [405] "Asplenium_aethiopicum"         "Asplenium_praemorsum"          "Asplenium_praegracile"         "Asplenium_volkensii"          
 [409] "Asplenium_hemitomum"           "Asplenium_friesiorum"          "Asplenium_punjabense"          "Asplenium_dalhousiae"         
 [413] "Asplenium_sagittatum"          "Asplenium_scolopendrium"       "Asplenium_hispanicum"          "Asplenium_ruta"               
 [417] "Asplenium_onopteris"           "Asplenium_cuneifolium"         "Asplenium_montanum"            "Asplenium_adiantum"           
 [421] "Asplenium_fissum"              "Asplenium_aegaeum"             "Asplenium_sulcatum"            "Asplenium_abscissum"          
 [425] "Asplenium_hostmannii"          "Asplenium_peruvianum"          "Asplenium_pteropus"            "Asplenium_cristatum"          
 [429] "Asplenium_myriophyllum"        "Asplenium_lunulatum"           "Asplenium_erectum"             "Asplenium_viride"             
 [433] "Asplenium_hobdyi"              "Asplenium_normale"             "Asplenium_trichomanes"         "Asplenium_tripteropus"        
 [437] "Asplenium_formosum"            "Asplenium_heterochroum"        "Asplenium_resiliens"           "Asplenium_monanthes"          
 [441] "Asplenium_hallbergii"          "Asplenium_dareoides"           "Asplenium_flabellifolium"      "Asplenium_pauperequitum"      
 [445] "Dicksonia_sellowiana"          "Asplenium_haughtonii"          "Asplenium_phillipsianum"       "Asplenium_cordatum"           
 [449] "Asplenium_interjectum"         "Asplenium_lushanense"          "Asplenium_laciniatum"          "Asplenium_varians"            
 [453] "Asplenium_tenuicaule"          "Asplenium_pekinense"           "Asplenium_sarelii"             "Asplenium_platyneuron"        
 [457] "Asplenium_bourgaei"            "Asplenium_jahandiezii"         "Asplenium_ruprechtii"          "Asplenium_radicans"           
 [461] "Asplenium_yunnanense"          "Asplenium_foresiense"          "Asplenium_incisum"             "Asplenium_petrarchae"         
 [465] "Asplenium_majoricum"           "Gymnocarpium_dryopteris"       "Acystopteris_tenuisecta"       "Cystopteris_fragilis"         
 [469] "Cystopteris_sudetica"          "Macrothelypteris_torresiana"   "Phegopteris_hexagonoptera"     "Thelypteris_confluens"        
 [473] "Oreopteris_limbosperma"        "Pronephrium_simplex"           "Parathelypteris_beddomei"      "Thelypteris_globulifera"      
 [477] "Thelypteris_noveboracensis"    "Diplazium_wichurae"            "Anisocampium_niponicum"        "Athyrium_schimperi"           
 [481] "Athyrium_yokoscense"           "Athyrium_alpestre"             "Athyrium_filix"                "Onoclea_sensibilis"           
 [485] "Matteuccia_struthiopteris"     "Blechnum_orientale"            "Sadleria_cyatheoides"          "Blechnum_spicant"             
 [489] "Blechnum_occidentale"          "Blechnum_patersonii"           "Blechnum_capense"              "Doodia_caudata"               
 [493] "Doodia_aspera"                 "Blechnum_medium"               "Lomariopsis_spectabilis"       "Nephrolepis_cordifolia"       
 [497] "Nephrolepis_hirsutula"         "Humata_pyxidata"               "Arthropteris_beckleri"         "Arthropteris_palisotii"       
 [501] "Arthropteris_orientalis"       "Arthropteris_monocarpa"        "Tectaria_devexa"               "Tectaria_yunnanensis"         
 [505] "Tectaria_decurrens"            "Tectaria_incisa"               "Tectaria_gaudichaudii"         "Tectaria_zeylanica"           
 [509] "Tectaria_impressa"             "Polypodium_australe"           "Polypodium_pellucidum"         "Polypodium_vulgare"           
 [513] "Polypodium_virginianum"        "Campyloneurum_brevifolium"     "Microgramma_lycopodioides"     "Microgramma_squamulosa"       
 [517] "Grammitis_baldwinii"           "Grammitis_diminuta"            "Grammitis_billardierei"        "Ctenopteris_heterophylla"     
 [521] "Grammitis_tenella"             "Adenophorus_pinnatifidus"      "Adenophorus_abietinus"         "Adenophorus_tamariscinus"     
 [525] "Platycerium_alcicorne"         "Platycerium_stemaria"          "Pyrrosia_adnascens"            "Pyrrosia_eleagnifolia"        
 [529] "Pyrrosia_serpens"              "Pleopeltis_polypodioides"      "Serpocaulon_triseriale"        "Bolbitis_heteroclita"         
 [533] "Colysis_hemitoma"              "Colysis_pentaphylla"           "Microsorum_punctatum"          "Phymatosorus_scolopendria"    
 [537] "Neocheiropteris_palmatopedata" "Neocheiropteris_ovata"         "Lepisorus_scolopendrius"       "Lepisorus_macrosphaerus"      
 [541] "Tricholepidium_maculosum"      "Lepisorus_thunbergianus"       "Lepisorus_contortus"           "Rumohra_adiantiformis"        
 [545] "Lastreopsis_decomposita"       "Lastreopsis_hispida"           "Elaphoglossum_peltatum"        "Elaphoglossum_hybridum"       
 [549] "Elaphoglossum_aubertii"        "Elaphoglossum_piloselloides"   "Elaphoglossum_acrostichoides"  "Elaphoglossum_petiolatum"     
 [553] "Elaphoglossum_marojejyense"    "Elaphoglossum_latifolium"      "Elaphoglossum_herminieri"      "Ctenitis_rhodolepis"          
 [557] "Cyrtomium_fortunei"            "Polystichum_tripteron"         "Polystichum_dielsii"           "Polystichum_luctuosum"        
 [561] "Polystichum_tsus"              "Polystichum_aculeatum"         "Polystichum_setiferum"         "Polystichum_pycnopterum"      
 [565] "Polystichum_eximium"           "Polystichum_neozelandicum"     "Polystichum_yunnanense"        "Polystichum_chilense"         
 [569] "Polystichum_munitum"           "Polystichum_acrostichoides"    "Polystichum_mohrioides"        "Polystichum_lonchitis"        
 [573] "Polystichum_montevidense"      "Polybotrya_caudata"            "Arachniodes_rhomboidea"        "Arachniodes_festina"          
 [577] "Arachniodes_simplicior"        "Arachniodes_globisora"         "Arachniodes_sporadosora"       "Acrophorus_stipellatus"       
 [581] "Dryopteris_stenolepis"         "Dryopteris_wallichiana"        "Dryopteris_lepidopoda"         "Dryopteris_oreades"           
 [585] "Dryopteris_filix"              "Dryopteris_marginalis"         "Dryopteris_chrysocoma"         "Dryopteris_pseudovaria"       
 [589] "Dryopteris_sublacera"          "Dryopteris_cochleata"          "Dryopteris_lacera"             "Dryopteris_aemula"            
 [593] "Nothoperanema_squamisetum"     "Nothoperanema_hendersonii"     "Dryopsis_mariformis"           "Dryopteris_setosa"            
 [597] "Dryopteris_fuscipes"           "Dryopteris_erythrosora"        "Dryopteris_sparsa"             "Dryopteris_juxtaposita"       
 [601] "Dryopteris_goldiana"           "Dryopteris_intermedia"         "Dryopteris_dilatata"           "Dryopteris_remota"            
 [605] "Dryopteris_carthusiana"        "Dryopteris_expansa"            "Dryopteris_cristata"           "Ginkgo_biloba"                
 [609] "Cycas_micholitzii"             "Cycas_multipinnata"            "Cycas_pectinata"               "Cycas_silvestris"             
 [613] "Cycas_condaoensis"             "Cycas_simplicipinna"           "Cycas_tanqingii"               "Cycas_szechuanensis"          
 [617] "Cycas_wadei"                   "Cycas_taiwaniana"              "Cycas_taitungensis"            "Cycas_revoluta"               
 [621] "Cycas_furfuracea"              "Cycas_hoabinhensis"            "Cycas_media"                   "Cycas_rumphii"                
 [625] "Cycas_lindstromii"             "Cycas_circinalis"              "Cycas_seemannii"               "Cycas_thouarsii"              
 [629] "Cycas_zeylanica"               "Cycas_edentata"                "Cycas_clivicola"               "Cycas_siamensis"              
 [633] "Cycas_pranburiensis"           "Cycas_tansachana"              "Cycas_chamaoensis"             "Cycas_nongnoochiae"           
 [637] "Cycas_segmentifida"            "Cycas_changjiangensis"         "Encephalartos_altensteinii"    "Dioon_spinulosum"             
 [641] "Dioon_mejiae"                  "Dioon_rzedowskii"              "Dioon_edule"                   "Dioon_tomasellii"             
 [645] "Dioon_purpusii"                "Dioon_holmgrenii"              "Dioon_merolae"                 "Dioon_califanoi"              
 [649] "Dioon_caputoi"                 "Stangeria_eriopus"             "Microcycas_calocoma"           "Zamia_poeppigiana"            
 [653] "Zamia_pumila"                  "Zamia_vazquezii"               "Zamia_restrepoi"               "Zamia_furfuracea"             
 [657] "Ceratozamia_miqueliana"        "Ceratozamia_robusta"           "Ceratozamia_microstrobila"     "Ceratozamia_norstogii"        
 [661] "Ceratozamia_kuesteriana"       "Ceratozamia_mexicana"          "Ceratozamia_sabatoi"           "Ceratozamia_euryphyllidia"    
 [665] "Ceratozamia_morettii"          "Ceratozamia_alvarezii"         "Ceratozamia_hildae"            "Bowenia_spectabilis"          
 [669] "Bowenia_serrulata"             "Macrozamia_fraseri"            "Macrozamia_miquelii"           "Macrozamia_platyrhachis"      
 [673] "Macrozamia_fawcettii"          "Macrozamia_reducta"            "Macrozamia_macdonnellii"       "Macrozamia_riedlei"           
 [677] "Macrozamia_glaucophylla"       "Macrozamia_polymorpha"         "Macrozamia_pauli"              "Macrozamia_communis"          
 [681] "Macrozamia_lucida"             "Macrozamia_moorei"             "Macrozamia_dyeri"              "Lepidozamia_hopei"            
 [685] "Lepidozamia_peroffskyana"      "Encephalartos_tegulaneus"      "Encephalartos_septentrionalis" "Encephalartos_bubalinus"      
 [689] "Encephalartos_kisambo"         "Encephalartos_macrostrobilus"  "Encephalartos_whitelockii"     "Encephalartos_gratus"         
 [693] "Encephalartos_hildebrandtii"   "Encephalartos_dyerianus"       "Encephalartos_latifrons"       "Encephalartos_transvenosus"   
 [697] "Encephalartos_munchii"         "Encephalartos_pterogonus"      "Encephalartos_humilis"         "Encephalartos_eugene"         
 [701] "Encephalartos_lanatus"         "Encephalartos_ghellinckii"     "Encephalartos_paucidentatus"   "Encephalartos_cycadifolius"   
 [705] "Encephalartos_laevifolius"     "Encephalartos_lebomboensis"    "Encephalartos_arenarius"       "Encephalartos_horridus"       
 [709] "Encephalartos_cupidus"         "Encephalartos_trispinosus"     "Encephalartos_inopinus"        "Encephalartos_princeps"       
 [713] "Encephalartos_lehmannii"       "Encephalartos_barteri"         "Encephalartos_manikensis"      "Encephalartos_longifolius"    
 [717] "Encephalartos_friderici"       "Encephalartos_villosus"        "Encephalartos_caffer"          "Encephalartos_woodii"         
 [721] "Encephalartos_natalensis"      "Encephalartos_ferox"           "Encephalartos_ngoyanus"        "Encephalartos_senticosus"     
 [725] "Encephalartos_heenanii"        "Encephalartos_umbeluziensis"   "Welwitschia_mirabilis"         "Gnetum_leyboldii"             
 [729] "Gnetum_nodiflorum"             "Gnetum_urens"                  "Gnetum_costatum"               "Gnetum_gnemon"                
 [733] "Gnetum_africanum"              "Gnetum_gnemonoides"            "Gnetum_parvifolium"            "Gnetum_montanum"              
 [737] "Gnetum_cuspidatum"             "Gnetum_macrostachyum"          "Gnetum_latifolium"             "Gnetum_ula"                   
 [741] "Gnetum_neglectum"              "Ephedra_tweediana"             "Ephedra_frustillata"           "Ephedra_chilensis"            
 [745] "Ephedra_nevadensis"            "Ephedra_torreyana"             "Ephedra_californica"           "Ephedra_aspera"               
 [749] "Ephedra_trifurca"              "Ephedra_viridis"               "Ephedra_gerardiana"            "Ephedra_equisetina"           
 [753] "Ephedra_przewalskii"           "Ephedra_minuta"                "Ephedra_pachyclada"            "Ephedra_sinica"               
 [757] "Ephedra_intermedia"            "Ephedra_distachya"             "Ephedra_regeliana"             "Ephedra_foeminea"             
 [761] "Ephedra_fragilis"              "Ephedra_foliata"               "Ephedra_alata"                 "Ephedra_aphylla"              
 [765] "Cedrus_libani"                 "Cedrus_atlantica"              "Cedrus_deodara"                "Keteleeria_davidiana"         
 [769] "Pseudolarix_amabilis"          "Nothotsuga_longibracteata"     "Tsuga_mertensiana"             "Tsuga_heterophylla"           
 [773] "Tsuga_dumosa"                  "Tsuga_diversifolia"            "Tsuga_caroliniana"             "Tsuga_sieboldii"              
 [777] "Tsuga_forrestii"               "Tsuga_chinensis"               "Keteleeria_evelyniana"         "Abies_bracteata"              
 [781] "Abies_firma"                   "Abies_fabri"                   "Abies_holophylla"              "Abies_fargesii"               
 [785] "Abies_fraseri"                 "Abies_koreana"                 "Abies_grandis"                 "Abies_recurvata"              
 [789] "Abies_nephrolepis"             "Abies_kawakamii"               "Abies_homolepis"               "Abies_procera"                
 [793] "Abies_amabilis"                "Abies_magnifica"               "Abies_alba"                    "Abies_nebrodensis"            
 [797] "Abies_nordmanniana"            "Abies_pinsapo"                 "Abies_numidica"                "Abies_veitchii"               
 [801] "Abies_cilicica"                "Abies_cephalonica"             "Pseudotsuga_sinensis"          "Pseudotsuga_macrocarpa"       
 [805] "Larix_gmelinii"                "Larix_decidua"                 "Larix_occidentalis"            "Larix_laricina"               
 [809] "Larix_sibirica"                "Larix_kaempferi"               "Larix_griffithii"              "Larix_mastersiana"            
 [813] "Larix_potaninii"               "Cathaya_argyrophylla"          "Picea_sitchensis"              "Picea_breweriana"             
 [817] "Picea_glauca"                  "Picea_engelmannii"             "Picea_pungens"                 "Picea_chihuahuana"            
 [821] "Picea_schrenkiana"             "Picea_spinulosa"               "Picea_likiangensis"            "Picea_orientalis"             
 [825] "Picea_alcoquiana"              "Picea_morrisonicola"           "Picea_brachytyla"              "Picea_purpurea"               
 [829] "Picea_wilsonii"                "Picea_glehnii"                 "Picea_jezoensis"               "Picea_rubens"                 
 [833] "Picea_omorika"                 "Picea_mariana"                 "Picea_abies"                   "Picea_asperata"               
 [837] "Picea_koyamae"                 "Picea_obovata"                 "Picea_meyeri"                  "Picea_koraiensis"             
 [841] "Picea_crassifolia"             "Picea_torano"                  "Picea_retroflexa"              "Pinus_nelsonii"               
 [845] "Pinus_balfouriana"             "Pinus_aristata"                "Pinus_monophylla"              "Pinus_quadrifolia"            
 [849] "Pinus_cembroides"              "Pinus_edulis"                  "Pinus_maximartinezii"          "Pinus_parviflora"             
 [853] "Pinus_fenzeliana"              "Pinus_armandii"                "Pinus_albicaulis"              "Pinus_lambertiana"            
 [857] "Abies_sibirica"                "Pinus_pumila"                  "Pinus_bhutanica"               "Pinus_monticola"              
 [861] "Pinus_peuce"                   "Pinus_krempfii"                "Pinus_gerardiana"              "Pinus_bungeana"               
 [865] "Pinus_koraiensis"              "Pinus_strobus"                 "Pinus_flexilis"                "Pinus_strobiformis"           
 [869] "Pinus_banksiana"               "Pinus_heldreichii"             "Pinus_canariensis"             "Pinus_brutia"                 
 [873] "Pinus_pinaster"                "Pinus_pinea"                   "Pinus_tropicalis"              "Pinus_massoniana"             
 [877] "Pinus_merkusii"                "Pinus_densiflora"              "Pinus_taiwanensis"             "Pinus_thunbergii"             
 [881] "Pinus_kesiya"                  "Pinus_densata"                 "Pinus_yunnanensis"             "Pinus_tabuliformis"           
 [885] "Pinus_hwangshanensis"          "Pinus_luchuensis"              "Pinus_clausa"                  "Pinus_virginiana"             
 [889] "Pinus_arizonica"               "Pinus_douglasiana"             "Pinus_coulteri"                "Pinus_engelmannii"            
 [893] "Pinus_jeffreyi"                "Pinus_devoniana"               "Pinus_hartwegii"               "Pinus_montezumae"             
 [897] "Pinus_sabiniana"               "Pinus_ayacahuite"              "Pinus_ponderosa"               "Pinus_torreyana"              
 [901] "Pinus_taeda"                   "Pinus_pungens"                 "Pinus_serotina"                "Pinus_muricata"               
 [905] "Pinus_attenuata"               "Pinus_glabra"                  "Pinus_teocote"                 "Pinus_leiophylla"             
 [909] "Pinus_greggii"                 "Pinus_lumholtzii"              "Pinus_lawsonii"                "Pinus_herrerae"               
 [913] "Pinus_pringlei"                "Pinus_oocarpa"                 "Pinus_cubensis"                "Pinus_palustris"              
 [917] "Pinus_caribaea"                "Pinus_elliottii"               "Pinus_echinata"                "Sciadopitys_verticillata"     
 [921] "Cephalotaxus_harringtonia"     "Cephalotaxus_fortunei"         "Cephalotaxus_harringtonii"     "Amentotaxus_yunnanensis"      
 [925] "Amentotaxus_argotaenia"        "Torreya_grandis"               "Torreya_californica"           "Torreya_taxifolia"            
 [929] "Torreya_nucifera"              "Austrotaxus_spicata"           "Pseudotaxus_chienii"           "Taxus_floridana"              
 [933] "Taxus_globosa"                 "Taxus_brevifolia"              "Taxus_x"                       "Taxus_wallichiana"            
 [937] "Taxus_canadensis"              "Taxus_cuspidata"               "Cunninghamia_lanceolata"       "Taiwania_cryptomerioides"     
 [941] "Metasequoia_glyptostroboides"  "Sequoiadendron_giganteum"      "Sequoia_sempervirens"          "Athrotaxis_laxifolia"         
 [945] "Athrotaxis_selaginoides"       "Athrotaxis_cupressoides"       "Cryptomeria_japonica"          "Glyptostrobus_pensilis"       
 [949] "Taxodium_huegelii"             "Taxodium_distichum"            "Papuacedrus_papuana"           "Austrocedrus_chilensis"       
 [953] "Libocedrus_plumosa"            "Pilgerodendron_uviferum"       "Libocedrus_yateensis"          "Libocedrus_bidwillii"         
 [957] "Diselma_archeri"               "Fitzroya_cupressoides"         "Widdringtonia_wallichii"       "Widdringtonia_nodiflora"      
 [961] "Widdringtonia_schwarzii"       "Callitris_preissii"            "Actinostrobus_pyramidalis"     "Actinostrobus_acuminatus"     
 [965] "Callitris_endlicheri"          "Callitris_rhomboidea"          "Thujopsis_dolabrata"           "Thuja_koraiensis"             
 [969] "Thuja_standishii"              "Thuja_plicata"                 "Chamaecyparis_obtusa"          "Chamaecyparis_lawsoniana"     
 [973] "Fokienia_hodginsii"            "Chamaecyparis_thyoides"        "Chamaecyparis_formosensis"     "Chamaecyparis_pisifera"       
 [977] "Tetraclinis_articulata"        "Platycladus_orientalis"        "Microbiota_decussata"          "Calocedrus_decurrens"         
 [981] "Calocedrus_macrolepis"         "Calocedrus_formosana"          "Cupressus_funebris"            "Cupressus_sempervirens"       
 [985] "Cupressus_chengiana"           "Cupressus_dupreziana"          "Cupressus_gigantea"            "Cupressus_duclouxiana"        
 [989] "Cupressus_cashmeriana"         "Cupressus_torulosa"            "Cupressus_nootkatensis"        "Cupressus_bakeri"             
 [993] "Cupressus_arizonica"           "Cupressus_lusitanica"          "Cupressus_guadalupensis"       "Cupressus_macnabiana"         
 [997] "Cupressus_macrocarpa"          "Cupressus_sargentii"           "Cupressus_goveniana"           "Juniperus_cedrus"             
 [ reached getOption("max.print") -- omitted 29499 entries ]

Bayesian update functions:

## Test 3 different functions. 
## 1. Step function with 3 parameters:
.updateStepFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- ifelse(midbins>pars.new$beta_center, pars.new$beta_right, pars.new$beta_left)
  return(list(pars=pars.new, hr=hr))
}
## 2. Sigmoid function with 5 parameters
.updateSigmoidFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_left + pars.new$beta_right/(1 + exp(pars.new$beta_slope * (midbins - pars.new$beta_center)))
  return(list(pars=pars.new, hr=hr))
}
## 3. Linear function with 2 parameters
.updateLinearFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_intercept + pars.new$beta_slope * midbins
  return(list(pars=pars.new, hr=hr))
}

Bayesian update functions for dealing with simmap reconstructions

## Other needed functions
.newMap <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  stree <- reorderSimmap(make.simmap(cache$phy, setNames(cache$pred[[1]], cache$phy$tip.label), Q=Q, message=FALSE), "postorder")
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  pars.new$sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}
.newMapPresample <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  index <- sample(1:length(strees.bayou), 1, replace=FALSE)
  pars.new$sb <- strees.bayou[[index]]$sb #unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- strees.bayou[[index]]$t2 #as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unname(strees.bayou[[index]]$loc) #unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}
## Empty functions to replace the deterministically specified theta values
dnull <- function(x, log=TRUE){
  if(log) 0 else 1
}
rnull <- function(x){
  return(x)
}

Place the predictor into bins and transform the tree to unit height.

## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])

td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat

Fit a meristic model to the binned predictor data and construct a set of stochastic maps.

## Use diversitree to fit a meristic model:
mkn.lik <- make.mkn.meristic(td$phy, td[['binpred']], k = ncat)
mkn.lik <- constrain(mkn.lik, q.up~q.down)
p <- c(0.1)
fit.mkn <- find.mle(mkn.lik, p)
Q <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q[i, i+1] <- fit.mkn$par[1]
  Q[i+1, i] <- fit.mkn$par[1]
}
diag(Q) <- apply(Q, 1, sum)*-1
rownames(Q) <- colnames(Q) <- 1:ncol(Q)
simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
td <- reorder(td, "postorder")
stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
make.simmap is sampling character histories conditioned on the transition matrix

Q =
          1          2          3          4          5          6          7          8
1 -9.886328   9.886328   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
2  9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000   0.000000   0.000000
3  0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000   0.000000
4  0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000
5  0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000
6  0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000
7  0.000000   0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328
8  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656
9  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   9.886328
          9
1  0.000000
2  0.000000
3  0.000000
4  0.000000
5  0.000000
6  0.000000
7  0.000000
8  9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
        1         2         3         4         5         6         7         8 
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 
        9 
0.1111111 
Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
strees <- make.simmap(td$phy, td[['binpred']], Q=Q, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix

Q =
          1          2          3          4          5          6          7          8
1 -9.886328   9.886328   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
2  9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000   0.000000   0.000000
3  0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000   0.000000
4  0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000   0.000000
5  0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000   0.000000
6  0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328   0.000000
7  0.000000   0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656   9.886328
8  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   9.886328 -19.772656
9  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   9.886328
          9
1  0.000000
2  0.000000
3  0.000000
4  0.000000
5  0.000000
6  0.000000
7  0.000000
8  9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
        1         2         3         4         5         6         7         8 
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 
        9 
0.1111111 
Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. 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Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. 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Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
for(i in 1:length(strees)) strees[[i]] <- reorderSimmap(strees[[i]], "postorder")
strees.bayou <- lapply(strees, simmap2bayou)
plotSimmap(stree, col=setNames(cm.colors(ncat), 1:ncat))

model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik
          [,1]
[1,] -439.3852
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unname(unlist(sapply(stree$maps, function(x) cumsum(x)[-1])))
tmp <- lm(td[[trait]]~td[[pred]]+I(td[[pred]]^2)+I(td[[pred]]^3))
strees.bayou <- readRDS("../output/OUwie/strees.bayou.rds")
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.step <- list(alpha=1, sig2=0.5, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.step$theta)

sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoid <- list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.sigmoid$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linear <- list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.linear$theta)

## Set priors
prior.step <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.sigmoid <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.linear <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
model.stepTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                beta_left=".updateStepFxTheta",
                                beta_right=".updateStepFxTheta",
                                beta_center=".updateStepFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)
model.stepTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
model.sigmoidTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_left=".updateSigmoidFxTheta",
                                beta_right=".updateSigmoidFxTheta",
                                beta_center=".updateSigmoidFxTheta",
                                beta_slope=".updateSigmoidFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)
model.sigmoidTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
model.linearTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_intercept=".updateLinearFxTheta",
                                beta_slope=".updateLinearFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_intercept=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_intercept=0.5,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_intercept", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)
model.linearTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_intercept", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_intercept, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
## Just a bunch of tests
cache <- bayou:::.prepare.ou.univariate(td$phy, td[['lnVs']], SE=0, pred=td['binpred'])
prior.step(startpar.step)
[1] -13.09416
model.stepTheta$lik.fn(startpar.step, cache, cache$dat)$loglik
          [,1]
[1,] -24543.32
ct <- bayou:::.buildControl(startpar.step, prior.step, move.weights=model.stepTheta$control.weights)
pars.new <- .newMapPresample(startpar.step, cache, d=model.stepTheta$D$theta, ct = ct)$pars
prior.step(pars.new)
[1] -13.09416
model.stepTheta$lik.fn(pars.new, cache, cache$dat)$loglik
          [,1]
[1,] -24558.23
pp <- pars.new
prior.step(pp)
[1] -13.09416
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik
          [,1]
[1,] -24558.23
new.pp <- .newMapPresample(pp, cache, 1, ct)$pars
model.stepTheta$lik.fn(new.pp, cache, cache$dat)$loglik
          [,1]
[1,] -24518.64
prior.linear(startpar.linear)
[1] -2.994012
new.pp <- .newMapPresample(startpar.linear, cache, 1, ct)$pars
model.linearTheta$lik.fn(new.pp, cache, cache$dat)$loglik
          [,1]
[1,] -24590.84
mymcmc.step <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.stepTheta, prior=prior.step, startpar=startpar.step, new.dir=TRUE, outname="stepTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.sigmoid <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoid, startpar=startpar.sigmoid, new.dir=TRUE, outname="sigmoidTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.linear <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linear, startpar=startpar.linear, new.dir=TRUE, outname="linearTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.step$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce k       .alpha  .beta_c .beta_l .beta_r .sig2   .theta  
2000    -590.84 -38.20  123.17  440.79  -6.55   -4.65   13.25   896     0.31    0.93    0.78    0.79    0.25    1.00    
4000    -592.19 -37.47  107.69  420.95  -6.31   -4.69   27.22   892     0.29    0.93    0.79    0.80    0.23    1.00    
6000    -583.48 -38.62  132.64  454.67  -6.53   -4.72   23.81   877     0.27    0.93    0.80    0.81    0.24    1.00    
8000    -589.63 -39.31  147.54  493.52  -6.26   -4.78   0.09    892     0.27    0.93    0.79    0.82    0.23    1.00    
10000   -588.58 -38.97  133.40  533.08  -6.32   -4.89   32.15   826     0.27    0.93    0.79    0.82    0.23    1.00    
12000   -583.27 -40.78  175.22  664.15  -6.36   -4.84   -13.44  852     0.26    0.93    0.78    0.82    0.23    1.00    
14000   -597.02 -41.23  189.28  677.93  -6.13   -4.62   62.50   894     0.26    0.93    0.78    0.83    0.23    1.00    
16000   -588.78 -38.49  133.22  420.87  -6.21   -4.11   97.16   855     0.27    0.93    0.78    0.83    0.23    1.00    
18000   -584.75 -39.28  143.07  522.83  -6.41   -4.52   1.94    874     0.26    0.92    0.79    0.83    0.24    1.00    
20000   -583.40 -41.06  187.33  642.00  -6.57   -4.67   13.89   867     0.26    0.93    0.79    0.83    0.23    1.00    
22000   -597.02 -39.85  150.75  612.46  -6.52   -4.87   -0.83   938     0.26    0.93    0.79    0.83    0.23    1.00    
24000   -592.27 -36.09  91.15   308.97  -6.29   -4.87   -19.24  864     0.26    0.93    0.79    0.83    0.23    1.00    
26000   -594.73 -37.87  119.58  400.65  -6.42   -4.87   24.79   869     0.25    0.93    0.79    0.83    0.23    1.00    
28000   -583.76 -37.74  115.39  408.31  -6.46   -4.76   28.95   849     0.25    0.92    0.79    0.83    0.23    1.00    
30000   -584.13 -39.36  145.15  526.49  -6.64   -4.80   28.89   852     0.25    0.92    0.79    0.83    0.23    1.00    
32000   -582.61 -39.04  142.30  470.36  -6.52   -4.71   33.03   836     0.25    0.92    0.79    0.83    0.23    1.00    
34000   -592.11 -38.49  129.83  446.70  -6.43   -4.84   -29.96  901     0.25    0.92    0.79    0.83    0.23    1.00    
36000   -612.74 -34.93  75.46   263.14  -6.29   -4.79   -4.08   853     0.25    0.92    0.79    0.83    0.24    1.00    
38000   -591.83 -37.88  116.14  432.02  -6.48   -4.60   28.81   873     0.25    0.92    0.79    0.83    0.24    1.00    
40000   -603.99 -39.44  146.89  532.45  -6.39   -4.93   -6.71   861     0.25    0.92    0.79    0.83    0.24    1.00    
42000   -586.39 -40.05  161.06  570.30  -6.33   -4.94   16.44   855     0.25    0.92    0.79    0.83    0.24    1.00    
44000   -583.98 -38.96  137.79  490.19  -6.57   -4.86   5.83    826     0.25    0.93    0.79    0.82    0.24    1.00    
46000   -584.42 -39.59  149.11  551.60  -6.48   -4.84   1.37    826     0.25    0.93    0.79    0.82    0.24    1.00    
48000   -597.49 -37.67  111.92  424.51  -6.28   -4.83   -7.83   878     0.25    0.93    0.79    0.82    0.24    1.00    
50000   -606.15 -39.68  152.47  545.14  -6.54   -4.73   26.50   853     0.25    0.93    0.79    0.82    0.24    1.00    
52000   -583.56 -39.35  148.20  496.32  -6.55   -4.86   26.14   826     0.25    0.93    0.79    0.82    0.24    1.00    
54000   -580.89 -42.45  231.74  728.12  -6.53   -4.70   14.94   925     0.25    0.93    0.79    0.82    0.24    1.00    
56000   -599.03 -38.91  133.52  515.63  -6.14   -4.79   -19.08  857     0.25    0.93    0.79    0.82    0.24    1.00    
58000   -581.65 -40.03  161.01  565.71  -6.43   -4.79   -25.00  925     0.25    0.93    0.79    0.83    0.24    1.00    
60000   -576.63 -42.11  220.94  698.27  -6.63   -4.79   -7.62   859     0.25    0.93    0.79    0.82    0.24    1.00    
62000   -590.18 -40.45  173.03  580.82  -6.38   -4.79   27.79   868     0.25    0.93    0.79    0.83    0.24    1.00    
64000   -594.47 -36.24  89.12   351.08  -6.56   -4.87   -3.13   892     0.25    0.93    0.79    0.83    0.24    1.00    
66000   -590.92 -46.53  394.08  1229.26 -6.61   -4.76   17.19   861     0.25    0.93    0.79    0.83    0.24    1.00    
68000   -588.47 -37.45  108.85  406.31  -6.62   -4.53   -11.20  826     0.25    0.93    0.79    0.83    0.24    1.00    
70000   -589.92 -40.52  168.51  645.45  -6.48   -4.68   -16.62  804     0.25    0.93    0.79    0.82    0.24    1.00    
72000   -592.50 -42.38  223.14  777.90  -6.38   -5.03   15.14   865     0.25    0.93    0.79    0.82    0.24    1.00    
74000   -590.80 -40.73  174.08  656.37  -6.56   -4.68   33.71   838     0.26    0.93    0.79    0.82    0.24    1.00    
76000   -591.83 -38.55  125.51  500.89  -6.43   -4.78   22.66   834     0.25    0.93    0.79    0.82    0.24    1.00    
78000   -590.42 -38.57  130.62  459.82  -6.51   -4.79   15.19   838     0.25    0.93    0.79    0.82    0.24    1.00    
80000   -583.16 -39.71  157.24  513.15  -6.48   -4.56   33.58   834     0.26    0.93    0.79    0.82    0.24    1.00    
82000   -582.41 -39.94  165.13  508.46  -6.57   -4.78   5.07    906     0.26    0.93    0.79    0.82    0.24    1.00    
84000   -593.76 -37.34  111.31  363.99  -6.48   -4.96   25.00   838     0.26    0.93    0.79    0.82    0.24    1.00    
86000   -588.90 -41.11  186.94  659.90  -6.49   -4.80   28.37   892     0.26    0.93    0.79    0.82    0.24    1.00    
88000   -580.48 -42.04  219.29  689.53  -6.45   -4.66   1.63    906     0.26    0.93    0.79    0.82    0.24    1.00    
90000   -591.94 -38.19  120.61  460.06  -6.26   -4.70   13.72   892     0.25    0.93    0.79    0.82    0.24    1.00    
92000   -593.83 -38.56  129.95  462.93  -6.47   -4.67   7.51    852     0.25    0.93    0.79    0.82    0.24    1.00    
94000   -590.57 -39.48  145.92  551.72  -6.45   -4.69   -3.98   865     0.25    0.93    0.79    0.82    0.24    1.00    
96000   -589.16 -40.19  167.17  558.78  -6.47   -4.80   -13.93  866     0.26    0.93    0.79    0.82    0.24    1.00    
98000   -591.75 -38.85  130.63  527.34  -6.54   -4.82   0.31    859     0.26    0.93    0.79    0.82    0.24    1.00    
100000  -592.66 -38.85  136.69  472.15  -6.39   -4.67   -21.49  859     0.25    0.93    0.79    0.82    0.24    1.00    
chain.step <- mymcmc.step$load()
chain.step <- set.burnin(chain.step, 0.3)
plot(chain.step)

plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.step$gen), 10), function(i) lines(x, ifelse(x>chain.step$beta_center[i], chain.step$beta_right[i], chain.step$beta_left[i]), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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mymcmc.sigmoid$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce beta_sl k       .alpha  .beta_c .beta_l .beta_r .beta_s .sig2   .theta  
2000    -579.39 -38.65  182.95  611.04  -2.13   -5.92   42.53   0.01    846     0.35    0.72    0.79    0.81    0.49    0.28    1.00    
4000    -557.49 -44.44  394.12  1199.24 -2.34   -6.12   17.40   0.01    807     0.31    0.75    0.75    0.82    0.40    0.25    1.00    
6000    -570.73 -38.65  184.94  590.22  -2.10   -5.73   61.63   0.01    896     0.30    0.76    0.73    0.82    0.38    0.24    1.00    
8000    -577.90 -39.54  214.36  686.73  -2.09   -5.19   85.80   0.01    846     0.28    0.75    0.72    0.81    0.39    0.25    1.00    
10000   -566.00 -43.96  371.12  1133.71 -2.59   -5.37   31.63   0.01    935     0.27    0.75    0.73    0.82    0.40    0.25    1.00    
12000   -570.57 -40.50  244.09  787.57  -3.18   -4.29   20.82   0.01    841     0.27    0.75    0.72    0.82    0.41    0.25    1.00    
14000   -572.54 -38.93  202.08  584.34  -3.44   -4.66   -29.00  0.01    895     0.27    0.74    0.72    0.82    0.43    0.24    1.00    
16000   -572.50 -38.42  184.63  548.14  -2.97   -5.07   -5.70   0.01    839     0.26    0.74    0.71    0.82    0.44    0.24    1.00    
18000   -570.38 -41.24  250.40  800.82  -2.42   -5.68   57.67   0.01    852     0.26    0.74    0.71    0.82    0.44    0.24    1.00    
20000   -568.21 -40.75  246.81  745.01  -2.47   -6.15   -4.04   0.01    925     0.26    0.73    0.71    0.82    0.43    0.24    1.00    
22000   -568.20 -36.89  146.95  442.62  -1.99   -6.15   55.13   0.01    867     0.25    0.73    0.70    0.82    0.42    0.25    1.00    
24000   -565.80 -40.40  245.53  750.51  -2.67   -5.09   44.04   0.01    855     0.25    0.73    0.71    0.82    0.42    0.24    1.00    
26000   -571.11 -43.28  337.83  1181.64 -2.85   -4.92   28.75   0.01    851     0.25    0.74    0.71    0.82    0.42    0.24    1.00    
28000   -557.80 -42.24  321.34  860.07  -3.35   -4.87   -22.20  0.01    807     0.26    0.74    0.71    0.82    0.42    0.24    1.00    
30000   -557.81 -43.16  374.55  903.59  -2.82   -4.41   61.17   0.01    878     0.26    0.74    0.71    0.82    0.43    0.24    1.00    
32000   -572.32 -41.79  285.87  951.22  -2.68   -4.69   48.10   0.01    857     0.26    0.74    0.71    0.82    0.44    0.24    1.00    
34000   -567.34 -40.93  258.66  850.16  -3.66   -3.98   -10.38  0.01    887     0.26    0.74    0.71    0.82    0.45    0.24    1.00    
36000   -568.31 -41.80  295.74  952.27  -3.82   -3.50   13.83   0.01    826     0.26    0.74    0.71    0.82    0.46    0.24    1.00    
38000   -562.73 -37.58  172.88  500.52  -4.21   -3.05   -9.93   0.02    906     0.26    0.74    0.71    0.82    0.47    0.24    1.00    
40000   -569.20 -39.07  210.42  633.06  -3.93   -2.83   29.50   0.02    895     0.26    0.74    0.71    0.82    0.49    0.24    1.00    
42000   -574.72 -40.30  243.84  783.93  -4.00   -3.03   10.00   0.02    846     0.26    0.74    0.71    0.82    0.50    0.24    1.00    
44000   -576.46 -34.75  110.14  367.11  -4.58   -3.19   -52.58  0.02    849     0.26    0.74    0.72    0.82    0.52    0.24    1.00    
46000   -579.26 -42.01  304.42  976.10  -4.33   -2.40   20.00   0.02    869     0.26    0.74    0.72    0.82    0.53    0.24    1.00    
48000   -570.93 -41.25  279.67  847.94  -3.88   -3.19   -2.91   0.01    866     0.26    0.74    0.72    0.82    0.54    0.25    1.00    
50000   -569.75 -39.11  199.65  624.50  -3.06   -4.78   11.91   0.01    868     0.25    0.74    0.72    0.82    0.54    0.25    1.00    
52000   -571.94 -43.49  364.57  1114.82 -3.54   -3.86   21.37   0.01    866     0.25    0.74    0.71    0.82    0.54    0.24    1.00    
54000   -574.76 -38.59  187.01  618.30  -2.94   -4.78   8.49    0.01    851     0.25    0.74    0.71    0.82    0.54    0.25    1.00    
56000   -579.37 -37.03  143.30  538.58  -3.62   -4.70   -49.23  0.01    854     0.25    0.75    0.72    0.82    0.54    0.25    1.00    
58000   -565.39 -39.86  232.52  673.89  -3.40   -3.94   29.58   0.01    836     0.25    0.74    0.72    0.82    0.54    0.25    1.00    
60000   -563.78 -39.90  236.18  631.31  -2.88   -5.03   26.07   0.01    873     0.25    0.74    0.72    0.82    0.54    0.25    1.00    
62000   -555.36 -41.65  303.76  825.78  -3.59   -3.37   31.83   0.01    836     0.25    0.74    0.72    0.82    0.54    0.25    1.00    
64000   -566.36 -38.48  191.88  545.73  -3.71   -4.06   -14.18  0.01    874     0.25    0.74    0.72    0.82    0.55    0.25    1.00    
66000   -578.59 -38.96  196.95  677.28  -3.58   -3.62   24.24   0.01    869     0.25    0.74    0.72    0.82    0.55    0.25    1.00    
68000   -579.21 -37.27  165.59  480.62  -4.45   -2.57   15.60   0.02    825     0.25    0.74    0.72    0.82    0.56    0.25    1.00    
70000   -571.85 -41.43  282.85  910.70  -4.37   -2.75   -9.26   0.02    838     0.25    0.75    0.72    0.82    0.56    0.24    1.00    
72000   -570.63 -39.79  229.36  654.21  -3.52   -3.95   13.79   0.01    810     0.25    0.75    0.72    0.82    0.57    0.24    1.00    
74000   -558.05 -37.51  172.64  486.45  -3.60   -3.58   25.53   0.02    836     0.25    0.74    0.72    0.82    0.57    0.24    1.00    
76000   -568.03 -36.78  153.02  455.04  -3.77   -3.57   5.19    0.01    879     0.25    0.74    0.72    0.82    0.57    0.24    1.00    
78000   -569.78 -40.68  248.48  842.61  -3.78   -3.64   14.95   0.01    834     0.25    0.74    0.72    0.82    0.57    0.25    1.00    
80000   -573.10 -39.64  228.50  673.36  -4.25   -2.86   -14.10  0.02    879     0.25    0.74    0.72    0.82    0.58    0.25    1.00    
82000   -559.90 -38.89  204.63  602.05  -4.01   -3.61   -21.88  0.01    818     0.25    0.74    0.72    0.82    0.59    0.25    1.00    
84000   -569.71 -37.99  179.09  565.55  -4.38   -2.75   -5.22   0.02    925     0.25    0.74    0.72    0.82    0.59    0.25    1.00    
86000   -555.41 -37.12  156.58  509.35  -4.10   -3.57   -27.69  0.01    788     0.25    0.74    0.72    0.82    0.59    0.24    1.00    
88000   -567.70 -40.01  242.16  675.87  -3.98   -3.18   4.88    0.01    925     0.25    0.75    0.72    0.82    0.60    0.24    1.00    
90000   -578.71 -41.73  280.42  954.27  -3.16   -4.54   7.05    0.01    859     0.25    0.75    0.72    0.82    0.60    0.25    1.00    
92000   -566.31 -43.36  347.88  1224.21 -3.53   -4.02   3.51    0.01    835     0.25    0.74    0.72    0.82    0.60    0.25    1.00    
94000   -574.04 -40.55  250.45  742.14  -3.11   -4.13   49.77   0.01    897     0.25    0.75    0.72    0.82    0.60    0.25    1.00    
96000   -578.37 -38.35  180.00  578.34  -3.83   -4.68   -76.81  0.01    882     0.25    0.75    0.72    0.82    0.60    0.25    1.00    
98000   -568.41 -41.33  276.13  822.04  -2.95   -4.92   16.90   0.01    810     0.25    0.75    0.72    0.82    0.60    0.25    1.00    
100000  -573.86 -45.99  488.74  1490.31 -2.78   -4.53   73.80   0.01    875     0.25    0.75    0.72    0.82    0.59    0.25    1.00    
chain.sigmoid <- mymcmc.sigmoid$load()
chain.sigmoid <- set.burnin(chain.sigmoid, 0.3)
plot(chain.sigmoid)

plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.sigmoid$gen), 1), function(i) lines(x, chain.sigmoid$beta_left[i]+chain.sigmoid$beta_right[i]/(1 + exp(chain.sigmoid$beta_slope[i]*(x-chain.sigmoid$beta_center[i]))), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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mymcmc.linear$run(100000)
gen     lnL     prior   alpha   sig2    beta_in beta_sl k       .alpha  .beta_i .beta_s .sig2   .theta  
2000    -572.10 -29.46  158.75  506.01  -5.53   0.01    811     0.35    0.50    0.47    0.26    1.00    
4000    -570.47 -37.62  481.10  1325.13 -5.50   0.01    858     0.30    0.45    0.46    0.25    1.00    
6000    -576.73 -30.28  173.30  615.18  -5.65   0.01    859     0.27    0.43    0.44    0.24    1.00    
8000    -572.35 -31.74  212.98  743.03  -5.66   0.01    866     0.26    0.43    0.44    0.25    1.00    
10000   -560.90 -33.58  282.48  839.97  -5.45   0.01    863     0.26    0.43    0.44    0.25    1.00    
12000   -571.84 -36.49  415.77  1181.50 -5.54   0.01    838     0.25    0.43    0.43    0.25    1.00    
14000   -561.23 -31.69  217.92  677.45  -5.58   0.01    851     0.25    0.43    0.42    0.25    1.00    
16000   -573.89 -29.52  157.71  536.20  -5.64   0.01    896     0.25    0.43    0.42    0.24    1.00    
18000   -569.20 -27.58  117.45  412.34  -5.50   0.01    849     0.26    0.43    0.42    0.24    1.00    
20000   -573.08 -31.78  213.79  753.12  -5.66   0.01    804     0.26    0.43    0.42    0.25    1.00    
22000   -577.22 -30.56  185.56  585.96  -5.50   0.01    866     0.26    0.43    0.42    0.25    1.00    
24000   -562.03 -29.33  156.51  490.26  -5.52   0.01    818     0.26    0.44    0.42    0.24    1.00    
26000   -567.11 -30.92  202.55  561.21  -5.59   0.01    877     0.26    0.43    0.42    0.25    1.00    
28000   -578.43 -30.63  189.43  574.16  -5.51   0.01    867     0.26    0.43    0.42    0.24    1.00    
30000   -568.75 -32.85  263.18  725.16  -5.62   0.01    866     0.26    0.43    0.42    0.25    1.00    
32000   -571.49 -35.74  367.99  1170.33 -5.57   0.01    876     0.26    0.43    0.41    0.25    1.00    
34000   -573.84 -28.03  137.66  351.82  -5.50   0.01    867     0.26    0.43    0.41    0.25    1.00    
36000   -575.74 -34.09  296.30  962.45  -5.60   0.01    868     0.26    0.43    0.41    0.25    1.00    
38000   -571.84 -31.04  202.19  597.22  -5.55   0.01    858     0.26    0.43    0.41    0.25    1.00    
40000   -577.03 -31.61  207.97  741.22  -5.63   0.01    866     0.26    0.43    0.41    0.25    1.00    
42000   -579.65 -28.25  131.47  436.53  -5.42   0.01    875     0.26    0.43    0.41    0.25    1.00    
44000   -574.60 -32.84  243.39  891.08  -5.56   0.01    878     0.26    0.43    0.41    0.25    1.00    
46000   -565.61 -33.11  263.26  810.82  -5.46   0.01    874     0.26    0.43    0.41    0.25    1.00    
48000   -581.27 -29.16  156.16  462.74  -5.79   0.01    871     0.26    0.43    0.41    0.25    1.00    
50000   -570.69 -29.89  172.00  515.81  -5.61   0.01    925     0.25    0.43    0.41    0.25    1.00    
52000   -578.44 -31.12  194.53  676.49  -5.42   0.01    897     0.26    0.43    0.41    0.25    1.00    
54000   -570.78 -29.75  166.21  522.91  -5.57   0.01    925     0.25    0.43    0.41    0.25    1.00    
56000   -576.91 -32.46  246.74  707.35  -5.53   0.01    861     0.25    0.43    0.41    0.25    1.00    
58000   -574.94 -30.98  196.68  627.54  -5.59   0.01    868     0.26    0.43    0.41    0.25    1.00    
60000   -573.95 -30.97  198.70  608.13  -5.64   0.01    838     0.26    0.43    0.41    0.25    1.00    
62000   -569.48 -33.97  292.57  937.03  -5.58   0.01    811     0.26    0.43    0.41    0.25    1.00    
64000   -584.18 -30.13  179.96  519.31  -5.68   0.01    869     0.25    0.43    0.41    0.25    1.00    
66000   -565.43 -31.99  229.64  683.73  -5.57   0.01    839     0.25    0.42    0.41    0.25    1.00    
68000   -568.29 -38.68  541.78  1557.41 -5.52   0.01    847     0.25    0.42    0.41    0.25    1.00    
70000   -580.56 -31.82  210.86  783.44  -5.48   0.01    860     0.25    0.43    0.41    0.25    1.00    
72000   -565.98 -28.59  137.80  468.08  -5.55   0.01    836     0.25    0.43    0.41    0.25    1.00    
74000   -567.81 -33.70  297.87  781.20  -5.60   0.01    879     0.25    0.43    0.41    0.25    1.00    
76000   -575.76 -33.98  285.42  1019.56 -5.66   0.01    854     0.25    0.43    0.41    0.25    1.00    
78000   -573.31 -32.87  257.81  767.95  -5.54   0.01    838     0.25    0.43    0.41    0.25    1.00    
80000   -564.28 -35.08  351.86  964.50  -5.60   0.01    849     0.25    0.42    0.41    0.25    1.00    
82000   -579.53 -28.43  138.87  429.24  -5.74   0.01    888     0.25    0.42    0.41    0.25    1.00    
84000   -565.66 -32.66  240.23  838.26  -5.48   0.01    873     0.25    0.42    0.41    0.25    1.00    
86000   -573.71 -28.59  136.79  476.51  -5.57   0.01    857     0.25    0.43    0.41    0.25    1.00    
88000   -576.16 -31.93  225.35  698.42  -5.57   0.01    842     0.25    0.43    0.41    0.25    1.00    
90000   -574.92 -31.29  206.03  632.96  -5.40   0.01    851     0.25    0.43    0.41    0.25    1.00    
92000   -571.82 -28.29  139.53  390.61  -5.54   0.01    874     0.25    0.42    0.41    0.25    1.00    
94000   -576.39 -31.20  201.83  654.59  -5.63   0.01    842     0.25    0.43    0.41    0.25    1.00    
96000   -575.75 -30.10  165.41  626.99  -5.50   0.01    896     0.25    0.43    0.41    0.25    1.00    
98000   -586.96 -28.71  145.72  437.10  -5.68   0.01    853     0.25    0.43    0.41    0.24    1.00    
100000  -572.65 -33.31  266.43  877.81  -5.56   0.01    876     0.25    0.43    0.41    0.25    1.00    
chain.linear <- mymcmc.linear$load()
chain.linear <- set.burnin(chain.linear, 0.3)
plot(chain.linear)

plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.linear$gen), 1), function(i) lines(x, chain.linear$beta_intercept[i]+chain.linear$beta_slope[i]*x, col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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plot(chain.step$lnL, type="n", ylim=c(-700, -500))
lines(1:length(chain.sigmoid$lnL), chain.sigmoid$lnL, col="green")
lines(1:length(chain.linear$lnL), chain.linear$lnL, col="blue")
lines(1:length(chain.step$lnL), chain.step$lnL, col="red")

saveRDS(chain.sigmoid, "../output/OUwie/chain.sigmoid")
saveRDS(chain.linear, "../output/OUwie/chain.linear")
saveRDS(chain.step, "../output/OUwie/chain.step")

Let’s do a likelihood based analysis.

library(optimx)
i <- 10
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), upper=c(1000, 1000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(1000, 1000, 0, 0, 200))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(1000, 1000, 0, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
Vy.linear <- sqrt(opt.linear$p2/(2*opt.linear$p1))
lines(x, opt.linear$p3+opt.linear$p4*x, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) + 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) - 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)

Vy.sigmoid <- opt.sigmoid$p2/(2*opt.sigmoid$p1)
lines(x, opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5))), col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) + 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) - 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)

Vy.step <- opt.step$p2/(2*opt.step$p1)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3), col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) + 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) - 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
## Script it over all trees. 
optimizeModelsOverSimmap <- function(i){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}

opt.allmodels <- lapply(1:100, optimizeModelsOverSimmap)
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)

    Paired t-test

data:  all.aics$aic.sigmoid and all.aics$aic.linear
t = -3.9995, df = 99, p-value = 0.0001225
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.6718993 -0.5630916
sample estimates:
mean of the differences 
              -1.117495 
tmp <- reshape2::melt(all.aics)
No id variables; using all as measure variables
cat("Akaike weights\n")
Akaike weights
aicws <- aicw(tmp$value)$w
round(tapply(aicws, tmp$variable, sum),2)
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
        0.52         0.00         0.00         0.42         0.00         0.05 
dAICs <- apply(all.aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))

boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC")

boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", ylim=c(0,8))

cat("Mean dAIC values\n")
Mean dAIC values
apply(t(dAICs), 2, mean)
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
   1.4509317   41.1330105    2.5684272    0.2571837   39.1330105    3.3319121 

Independent analyses for Deciduous vs. Evergreen

Let’s filter by phenology and analyze each independently. This is not ideal, since transitions occur between deciduous and evergreen species, and are not accounted for when we filter this way. However, if phylogenetic half-lives are small, this is probably of negligible importance.

## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])

bins
 [1] -395.10000 -329.11667 -263.13333 -197.15000 -131.16667  -65.18333    0.80000   66.78333  132.76667  198.75000
td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat
tdEV <- filter(td, phenology == "EV")
tdD <- filter(td, phenology == "D")
tdEV$phy <- reorder(tdEV$phy, "postorder")
tdD$phy <- reorder(tdD$phy, "postorder")
#tdEV$phy$edge.length <- tdEV$phy$edge.length/max(branching.times(tdEV$phy))*100
## Use diversitree to fit a meristic model:
mknEV.lik <- make.mkn.meristic(tdEV$phy, tdEV[['binpred']], k = ncat)
mknD.lik <- make.mkn.meristic(tdD$phy, tdD[['binpred']], k = ncat)
mknEV.lik <- constrain(mknEV.lik, q.up~q.down)
mknD.lik <- constrain(mknD.lik, q.up~q.down)
p <- c(0.1)
EVfit.mkn <- find.mle(mknEV.lik, p)
Dfit.mkn <- find.mle(mknD.lik, p)
Q.EV <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.EV[i, i+1] <- EVfit.mkn$par[1]
  Q.EV[i+1, i] <- EVfit.mkn$par[1]
}
Q.D <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.D[i, i+1] <- Dfit.mkn$par[1]
  Q.D[i+1, i] <- Dfit.mkn$par[1]
}
diag(Q.EV) <- apply(Q.EV, 1, sum)*-1
diag(Q.D) <- apply(Q.D, 1, sum)*-1
rownames(Q.EV) <- colnames(Q.EV) <- 1:ncol(Q.EV)
rownames(Q.D) <- colnames(Q.D) <- 1:ncol(Q.D)
simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
#stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees.EV <- make.simmap(tdEV$phy, tdEV[['binpred']], Q=Q.EV, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix

Q =
          1         2         3         4         5         6         7         8         9
1 -4.891797  4.891797  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
2  4.891797 -9.783594  4.891797  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
3  0.000000  4.891797 -9.783594  4.891797  0.000000  0.000000  0.000000  0.000000  0.000000
4  0.000000  0.000000  4.891797 -9.783594  4.891797  0.000000  0.000000  0.000000  0.000000
5  0.000000  0.000000  0.000000  4.891797 -9.783594  4.891797  0.000000  0.000000  0.000000
6  0.000000  0.000000  0.000000  0.000000  4.891797 -9.783594  4.891797  0.000000  0.000000
7  0.000000  0.000000  0.000000  0.000000  0.000000  4.891797 -9.783594  4.891797  0.000000
8  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  4.891797 -9.783594  4.891797
9  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  4.891797 -4.891797
(specified by the user);
and (mean) root node prior probabilities
pi =
        1         2         3         4         5         6         7         8         9 
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 
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Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
strees.D <- make.simmap(tdD$phy, tdD[['binpred']], Q=Q.D, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix

Q =
          1         2         3         4         5         6         7         8         9
1 -12.19336  12.19336   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
2  12.19336 -24.38672  12.19336   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
3   0.00000  12.19336 -24.38672  12.19336   0.00000   0.00000   0.00000   0.00000   0.00000
4   0.00000   0.00000  12.19336 -24.38672  12.19336   0.00000   0.00000   0.00000   0.00000
5   0.00000   0.00000   0.00000  12.19336 -24.38672  12.19336   0.00000   0.00000   0.00000
6   0.00000   0.00000   0.00000   0.00000  12.19336 -24.38672  12.19336   0.00000   0.00000
7   0.00000   0.00000   0.00000   0.00000   0.00000  12.19336 -24.38672  12.19336   0.00000
8   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000  12.19336 -24.38672  12.19336
9   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000  12.19336 -12.19336
(specified by the user);
and (mean) root node prior probabilities
pi =
        1         2         3         4         5         6         7         8         9 
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 
Done.
for(i in 1:length(strees.EV)) strees.EV[[i]] <- reorderSimmap(strees.EV[[i]], "postorder")
for(i in 1:length(strees.D)) strees.D[[i]] <- reorderSimmap(strees.D[[i]], "postorder")
EVtrees.bayou <- lapply(strees.EV, simmap2bayou)
Dtrees.bayou <- lapply(strees.D, simmap2bayou)
saveRDS(EVtrees.bayou, file="../output/OUwie/EVtrees.rds")
saveRDS(Dtrees.bayou, file="../output/OUwie/Dtrees.rds")
EVtrees.bayou <- readRDS(file="../output/OUwie/EVtrees.rds")
Dtrees.bayou <- readRDS(file="../output/OUwie/Dtrees.rds")
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.stepEVD <- list(
  EV = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2), 
  D = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.stepEVD$EV$theta)

sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoidEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.sigmoidEVD$EV$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linearEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.linearEVD$EV$theta)

## Set priors
EV.fixed <- list(k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
D.fixed <- list(k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
prior.stepEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
  
prior.sigmoidEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
prior.linearEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
mymcmcEV.step <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$EV, startpar=startpar.stepEVD$EV, new.dir=TRUE, outname="stepThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.step <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$D, startpar=startpar.stepEVD$D, new.dir=TRUE, outname="stepThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.sigmoid <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$EV, startpar=startpar.sigmoidEVD$EV, new.dir=TRUE, outname="sigmoidThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.sigmoid <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$D, startpar=startpar.sigmoidEVD$D, new.dir=TRUE, outname="sigmoidThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.linear <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$EV, startpar=startpar.linearEVD$EV, new.dir=TRUE, outname="linearThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.linear <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$D, startpar=startpar.linearEVD$D, new.dir=TRUE, outname="linearThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
strees.bayou <- EVtrees.bayou
mymcmcEV.step$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce k       .alpha  .beta_c .beta_l .beta_r .sig2   .theta  
1000    -389.49 -37.84  123.25  367.89  -7.03   -4.87   -25.93  347     0.40    0.89    0.83    0.84    0.36    1.00    
2000    -391.58 -43.97  278.06  939.88  -6.87   -5.03   -8.55   339     0.37    0.93    0.83    0.82    0.31    1.00    
3000    -392.95 -39.24  148.78  464.69  -6.78   -4.85   -10.20  324     0.35    0.91    0.82    0.82    0.31    1.00    
4000    -383.60 -40.84  195.60  511.91  -6.80   -4.58   -11.58  370     0.34    0.91    0.83    0.82    0.32    1.00    
5000    -403.00 -40.82  174.01  689.39  -6.66   -4.95   22.47   351     0.33    0.91    0.82    0.83    0.33    1.00    
6000    -384.36 -39.70  157.54  507.07  -6.81   -4.59   -6.65   370     0.33    0.92    0.84    0.84    0.33    1.00    
7000    -389.76 -43.39  262.23  829.82  -6.70   -4.88   13.86   325     0.33    0.90    0.84    0.83    0.33    1.00    
8000    -395.28 -40.45  172.14  588.95  -6.83   -4.85   -19.79  360     0.32    0.91    0.84    0.83    0.32    1.00    
9000    -402.95 -45.02  314.84  1115.19 -6.76   -5.07   -9.86   364     0.32    0.91    0.83    0.83    0.32    1.00    
10000   -386.46 -43.48  258.10  906.12  -6.78   -4.88   14.43   365     0.32    0.91    0.83    0.83    0.31    1.00    
11000   -393.09 -46.57  375.87  1446.57 -6.80   -4.94   24.70   348     0.32    0.91    0.83    0.83    0.31    1.00    
12000   -395.50 -40.75  175.73  649.71  -6.99   -5.00   22.85   331     0.32    0.91    0.83    0.83    0.31    1.00    
13000   -389.45 -41.84  201.86  779.10  -6.74   -4.76   29.74   326     0.32    0.91    0.83    0.82    0.31    1.00    
14000   -390.01 -42.50  231.29  750.37  -6.69   -4.81   19.73   348     0.32    0.91    0.83    0.82    0.31    1.00    
15000   -400.90 -42.79  244.14  748.07  -6.66   -4.85   32.51   358     0.32    0.91    0.84    0.82    0.30    1.00    
16000   -385.66 -42.80  243.25  760.22  -6.70   -4.75   27.84   332     0.32    0.91    0.84    0.82    0.30    1.00    
17000   -400.21 -39.10  139.64  508.42  -7.04   -4.81   15.16   351     0.31    0.91    0.84    0.82    0.30    1.00    
18000   -393.84 -42.58  224.19  848.50  -6.49   -4.66   16.79   350     0.31    0.91    0.84    0.82    0.30    1.00    
19000   -395.55 -44.98  313.61  1103.84 -6.80   -4.89   15.81   350     0.31    0.91    0.84    0.82    0.30    1.00    
20000   -398.85 -39.82  153.06  580.66  -6.89   -4.79   -21.83  342     0.31    0.91    0.84    0.82    0.30    1.00    
21000   -396.40 -42.09  209.37  800.72  -6.89   -4.79   9.78    357     0.31    0.92    0.84    0.82    0.29    1.00    
22000   -389.66 -44.13  281.43  984.07  -6.78   -4.78   17.18   325     0.31    0.92    0.84    0.83    0.30    1.00    
23000   -413.53 -38.11  128.30  380.73  -6.69   -4.83   17.46   352     0.31    0.92    0.84    0.83    0.30    1.00    
24000   -398.98 -40.60  176.04  597.82  -6.67   -4.77   -19.42  358     0.31    0.92    0.84    0.83    0.30    1.00    
25000   -406.83 -40.81  178.14  644.24  -6.85   -4.81   17.23   333     0.31    0.92    0.84    0.83    0.30    1.00    
26000   -391.49 -42.63  232.31  793.36  -6.80   -4.60   10.93   348     0.31    0.92    0.84    0.83    0.30    1.00    
27000   -406.05 -39.63  148.27  571.46  -6.69   -4.93   -6.03   364     0.31    0.92    0.84    0.83    0.30    1.00    
28000   -392.42 -41.14  198.64  571.78  -6.90   -5.13   -20.97  353     0.31    0.92    0.84    0.83    0.30    1.00    
29000   -400.75 -40.46  169.53  616.89  -6.85   -4.79   4.53    333     0.31    0.92    0.84    0.83    0.30    1.00    
30000   -393.68 -40.38  176.09  535.25  -6.79   -4.94   11.73   313     0.31    0.92    0.84    0.83    0.30    1.00    
31000   -392.74 -41.88  222.37  612.96  -6.79   -4.83   21.27   341     0.31    0.92    0.84    0.83    0.30    1.00    
32000   -385.88 -41.96  223.52  629.56  -6.77   -4.78   -18.64  361     0.31    0.92    0.84    0.83    0.30    1.00    
33000   -401.70 -39.50  140.93  606.62  -6.89   -4.68   -18.78  339     0.31    0.92    0.84    0.83    0.30    1.00    
34000   -387.65 -41.48  207.75  601.08  -6.90   -4.68   -21.06  365     0.31    0.92    0.84    0.83    0.30    1.00    
35000   -393.17 -41.79  216.03  634.00  -6.78   -4.77   26.11   332     0.31    0.92    0.84    0.83    0.30    1.00    
36000   -388.83 -41.93  206.06  769.54  -6.80   -4.83   -1.35   330     0.31    0.92    0.84    0.83    0.30    1.00    
37000   -397.23 -42.16  215.54  765.58  -6.84   -4.44   -14.63  342     0.31    0.92    0.84    0.83    0.30    1.00    
38000   -408.26 -38.70  133.86  462.40  -6.70   -4.92   23.39   333     0.31    0.92    0.84    0.83    0.30    1.00    
39000   -386.80 -40.06  166.35  529.57  -6.79   -4.59   -30.30  327     0.31    0.92    0.84    0.83    0.30    1.00    
40000   -401.33 -44.19  294.21  895.00  -6.52   -4.99   31.11   358     0.31    0.92    0.84    0.83    0.30    1.00    
41000   -393.62 -42.32  219.07  796.21  -6.68   -4.80   -3.42   348     0.31    0.92    0.84    0.83    0.30    1.00    
42000   -394.74 -40.80  182.10  605.73  -6.79   -4.73   -15.75  360     0.31    0.92    0.84    0.83    0.30    1.00    
43000   -384.84 -43.36  266.37  780.68  -6.79   -4.77   -1.18   332     0.31    0.92    0.84    0.83    0.30    1.00    
44000   -378.56 -45.21  333.69  1035.86 -6.72   -4.79   3.28    370     0.31    0.92    0.84    0.83    0.30    1.00    
45000   -396.24 -46.72  400.07  1290.01 -6.95   -4.68   -7.82   336     0.31    0.92    0.84    0.83    0.30    1.00    
46000   -393.99 -40.11  165.30  550.41  -6.71   -4.96   4.05    375     0.31    0.92    0.84    0.83    0.30    1.00    
47000   -388.50 -42.13  216.93  740.60  -6.65   -4.93   2.32    349     0.31    0.92    0.84    0.83    0.30    1.00    
48000   -397.35 -44.46  305.00  925.52  -6.75   -5.04   -24.27  333     0.31    0.92    0.84    0.83    0.30    1.00    
49000   -406.00 -42.87  232.42  892.00  -6.89   -4.94   -3.16   378     0.31    0.92    0.84    0.83    0.29    1.00    
50000   -395.63 -49.18  542.50  1732.73 -6.62   -4.70   26.58   326     0.31    0.92    0.84    0.83    0.29    1.00    
51000   -395.34 -41.23  184.91  722.11  -6.74   -4.87   4.33    346     0.31    0.92    0.84    0.83    0.29    1.00    
52000   -393.86 -43.92  275.44  942.99  -6.75   -5.01   -2.60   357     0.31    0.92    0.84    0.83    0.29    1.00    
53000   -402.29 -38.98  131.80  552.64  -6.73   -4.99   -16.72  333     0.31    0.92    0.84    0.83    0.29    1.00    
54000   -390.22 -39.02  145.83  437.62  -6.76   -4.85   -4.37   361     0.31    0.92    0.84    0.83    0.29    1.00    
55000   -393.85 -41.81  207.54  711.17  -6.90   -4.83   31.00   332     0.31    0.93    0.84    0.83    0.29    1.00    
56000   -389.99 -46.97  410.00  1361.11 -6.84   -4.76   -24.89  375     0.31    0.93    0.84    0.83    0.29    1.00    
57000   -390.06 -44.38  295.97  966.77  -6.89   -4.68   -5.27   344     0.31    0.93    0.84    0.83    0.29    1.00    
58000   -390.89 -43.05  244.40  848.59  -6.77   -4.72   -12.91  341     0.31    0.93    0.84    0.83    0.29    1.00    
59000   -395.00 -48.49  478.47  1816.41 -6.84   -4.73   17.38   360     0.31    0.92    0.84    0.83    0.29    1.00    
60000   -397.08 -41.51  198.91  687.09  -7.10   -4.75   19.48   346     0.31    0.93    0.84    0.83    0.29    1.00    
61000   -395.70 -42.86  252.39  706.34  -6.79   -4.98   24.44   350     0.31    0.93    0.84    0.83    0.30    1.00    
62000   -386.34 -44.78  309.56  1040.87 -6.72   -4.91   22.96   330     0.31    0.93    0.84    0.83    0.30    1.00    
63000   -385.14 -43.25  256.21  825.96  -6.70   -4.85   20.95   334     0.31    0.93    0.84    0.83    0.30    1.00    
64000   -402.62 -41.89  221.60  620.55  -6.76   -4.74   28.11   352     0.31    0.93    0.84    0.83    0.30    1.00    
65000   -396.89 -44.53  304.26  961.51  -6.54   -4.79   20.47   320     0.31    0.93    0.84    0.83    0.30    1.00    
66000   -378.04 -45.15  334.87  995.20  -6.75   -4.77   -8.92   370     0.31    0.93    0.84    0.83    0.29    1.00    
67000   -393.12 -41.94  209.97  737.75  -6.56   -4.68   11.97   375     0.31    0.92    0.84    0.83    0.30    1.00    
68000   -390.80 -41.78  212.03  661.95  -6.87   -4.85   15.86   324     0.31    0.92    0.84    0.83    0.29    1.00    
69000   -397.92 -39.92  165.58  500.23  -6.62   -4.64   15.76   343     0.31    0.92    0.84    0.83    0.29    1.00    
70000   -404.76 -45.58  339.91  1185.09 -6.60   -4.69   -23.19  333     0.31    0.92    0.84    0.83    0.29    1.00    
71000   -386.45 -41.38  208.54  565.43  -6.86   -4.84   6.24    327     0.31    0.92    0.84    0.83    0.29    1.00    
72000   -401.90 -39.05  136.13  528.74  -6.84   -4.68   9.90    342     0.31    0.92    0.84    0.83    0.29    1.00    
73000   -414.37 -36.12  89.74   325.58  -6.58   -5.16   22.22   333     0.31    0.92    0.84    0.83    0.29    1.00    
74000   -389.69 -44.12  293.98  864.97  -6.76   -4.86   17.09   344     0.31    0.92    0.84    0.83    0.29    1.00    
75000   -389.78 -42.20  214.08  795.76  -6.75   -4.88   -17.28  353     0.31    0.92    0.84    0.83    0.29    1.00    
76000   -391.23 -41.09  187.42  650.00  -6.84   -4.59   -14.33  353     0.31    0.92    0.84    0.83    0.29    1.00    
77000   -391.60 -41.85  199.04  813.63  -6.75   -4.97   -23.95  365     0.31    0.92    0.84    0.83    0.29    1.00    
78000   -379.49 -47.37  431.10  1427.52 -6.86   -4.97   -26.45  370     0.31    0.92    0.84    0.83    0.29    1.00    
79000   -384.61 -46.11  372.18  1180.43 -6.64   -4.88   31.74   334     0.31    0.92    0.84    0.83    0.29    1.00    
80000   -384.44 -47.68  442.42  1544.49 -6.75   -4.77   -8.00   334     0.31    0.92    0.84    0.83    0.29    1.00    
81000   -389.82 -40.40  175.85  544.47  -6.85   -4.96   2.74    353     0.31    0.92    0.84    0.83    0.29    1.00    
82000   -387.05 -42.59  238.91  720.25  -6.79   -4.60   -21.93  349     0.31    0.92    0.84    0.83    0.29    1.00    
83000   -391.86 -44.26  282.39  1040.74 -6.95   -4.88   13.49   360     0.31    0.92    0.84    0.83    0.29    1.00    
84000   -390.86 -40.65  177.18  605.51  -6.71   -4.71   -1.73   353     0.31    0.92    0.84    0.83    0.29    1.00    
85000   -394.09 -40.74  183.19  580.14  -6.91   -4.80   0.43    332     0.31    0.92    0.84    0.83    0.29    1.00    
86000   -393.36 -40.68  178.91  597.94  -6.96   -4.95   -21.63  348     0.31    0.92    0.84    0.83    0.29    1.00    
87000   -409.43 -37.88  113.33  457.79  -6.57   -5.13   -23.85  378     0.31    0.92    0.84    0.83    0.29    1.00    
88000   -397.91 -41.66  208.74  650.50  -6.67   -5.12   -15.26  357     0.31    0.92    0.84    0.83    0.29    1.00    
89000   -403.88 -43.81  265.21  991.54  -6.61   -4.68   -17.81  364     0.31    0.92    0.84    0.83    0.29    1.00    
90000   -390.30 -40.00  168.48  496.10  -6.79   -4.74   10.70   371     0.31    0.92    0.84    0.83    0.29    1.00    
91000   -393.33 -39.93  156.78  574.93  -6.74   -4.74   1.36    348     0.31    0.92    0.84    0.83    0.29    1.00    
92000   -392.35 -48.18  512.55  1254.55 -6.75   -4.96   -5.62   339     0.31    0.92    0.84    0.83    0.29    1.00    
93000   -392.23 -42.94  239.62  849.17  -6.77   -5.10   -7.29   342     0.31    0.92    0.84    0.83    0.29    1.00    
94000   -403.08 -45.86  339.64  1367.28 -6.48   -4.85   14.54   364     0.31    0.92    0.84    0.83    0.29    1.00    
95000   -401.07 -39.77  150.27  591.64  -6.96   -4.85   21.10   339     0.31    0.92    0.84    0.83    0.29    1.00    
96000   -391.10 -46.58  384.20  1360.43 -6.71   -4.81   -24.22  332     0.31    0.92    0.84    0.83    0.29    1.00    
97000   -399.44 -41.16  188.94  657.80  -6.70   -5.00   -12.29  342     0.31    0.92    0.84    0.83    0.29    1.00    
98000   -386.43 -40.75  186.23  557.66  -6.92   -4.74   1.27    346     0.31    0.93    0.84    0.83    0.29    1.00    
99000   -404.54 -38.42  131.17  422.74  -6.83   -4.90   -8.60   321     0.31    0.93    0.84    0.83    0.29    1.00    
100000  -402.55 -43.67  261.22  965.18  -6.60   -4.84   19.49   364     0.31    0.93    0.84    0.83    0.29    1.00    
chainEV.step <- mymcmcEV.step$load()
chainEV.step <- set.burnin(chainEV.step, 0.3)
plot(chainEV.step)

strees.bayou <- Dtrees.bayou
mymcmcD.step$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce k       .alpha  .beta_c .beta_l .beta_r .sig2   .theta  
1000    -171.01 -25.69  15.15   43.81   -6.74   -4.35   -96.48  423     0.55    0.98    0.91    0.93    0.37    1.00    
2000    -174.17 -24.39  10.40   36.71   -6.07   -4.48   13.69   423     0.53    0.97    0.89    0.94    0.35    1.00    
3000    -177.91 -23.60  9.21    28.46   -6.30   -4.49   -77.11  352     0.49    0.97    0.87    0.93    0.36    1.00    
4000    -177.86 -24.82  12.69   35.63   -6.05   -5.00   -62.49  372     0.48    0.93    0.87    0.92    0.37    1.00    
5000    -165.96 -25.87  17.34   39.70   -6.70   -4.49   -72.05  390     0.48    0.93    0.88    0.92    0.37    1.00    
6000    -177.56 -23.84  8.79    33.75   -5.68   -3.78   -1.20   396     0.47    0.93    0.88    0.91    0.38    1.00    
7000    -183.75 -23.20  6.77    31.93   -6.13   -3.79   60.75   437     0.47    0.93    0.88    0.92    0.38    1.00    
8000    -183.96 -24.31  11.27   32.03   -5.38   -3.66   59.95   390     0.48    0.93    0.88    0.93    0.38    1.00    
9000    -178.99 -23.83  9.35    31.28   -5.87   -3.97   91.09   404     0.48    0.93    0.88    0.93    0.38    1.00    
10000   -175.59 -25.05  12.64   40.35   -6.05   -4.12   -27.01  372     0.48    0.93    0.88    0.93    0.39    1.00    
11000   -178.08 -27.55  21.27   67.81   -5.91   -4.17   -27.87  391     0.48    0.92    0.88    0.93    0.39    1.00    
12000   -179.36 -24.02  8.60    37.84   -5.78   -4.94   -56.76  379     0.48    0.93    0.88    0.93    0.39    1.00    
13000   -177.17 -25.21  12.54   44.00   -5.67   -4.47   60.52   388     0.48    0.93    0.89    0.93    0.39    1.00    
14000   -177.63 -24.62  12.36   33.44   -5.89   -3.76   69.03   409     0.48    0.93    0.89    0.93    0.39    1.00    
15000   -175.11 -27.62  21.29   70.33   -6.21   -4.73   -69.72  374     0.48    0.93    0.90    0.93    0.39    1.00    
16000   -181.41 -24.02  8.85    36.57   -6.74   -4.62   -67.26  420     0.48    0.93    0.90    0.93    0.39    1.00    
17000   -177.04 -24.80  11.82   38.67   -6.22   -4.71   -86.54  409     0.48    0.93    0.89    0.93    0.40    1.00    
18000   -177.77 -22.22  5.86    22.40   -6.75   -3.87   -64.37  398     0.48    0.93    0.90    0.93    0.40    1.00    
19000   -172.44 -25.47  13.64   45.03   -6.53   -4.55   -38.13  357     0.48    0.93    0.90    0.93    0.40    1.00    
20000   -176.64 -24.11  9.72    34.52   -6.46   -4.58   -79.74  372     0.48    0.93    0.90    0.93    0.40    1.00    
21000   -175.20 -24.51  11.37   35.00   -6.01   -4.57   -4.09   403     0.48    0.94    0.90    0.93    0.40    1.00    
22000   -178.86 -23.34  8.16    28.52   -6.11   -4.12   33.78   386     0.47    0.94    0.90    0.93    0.40    1.00    
23000   -176.80 -23.69  8.87    30.92   -6.48   -3.87   15.17   406     0.47    0.94    0.90    0.93    0.40    1.00    
24000   -182.82 -23.11  7.92    26.17   -5.61   -3.64   93.69   435     0.47    0.94    0.90    0.93    0.40    1.00    
25000   -186.23 -22.92  5.70    32.62   -5.29   -2.88   57.97   392     0.47    0.94    0.90    0.93    0.40    1.00    
26000   -179.14 -23.50  7.91    31.74   -5.68   -4.16   42.02   366     0.47    0.94    0.90    0.93    0.40    1.00    
27000   -177.35 -23.01  7.57    26.05   -5.67   -4.59   20.69   381     0.47    0.94    0.90    0.93    0.40    1.00    
28000   -179.55 -22.86  6.56    27.85   -5.87   -3.95   4.11    401     0.47    0.94    0.90    0.93    0.40    1.00    
29000   -182.47 -22.01  5.71    20.58   -5.69   -3.90   95.86   391     0.48    0.94    0.90    0.93    0.40    1.00    
30000   -181.10 -23.45  7.05    34.84   -5.85   -4.65   68.03   379     0.48    0.94    0.90    0.93    0.40    1.00    
31000   -180.90 -22.87  6.21    29.34   -6.03   -4.48   29.61   374     0.48    0.94    0.90    0.93    0.40    1.00    
32000   -184.70 -26.22  16.00   52.82   -5.82   -4.13   -3.89   404     0.48    0.94    0.90    0.93    0.40    1.00    
33000   -177.09 -25.42  14.86   39.21   -6.77   -5.19   -98.59  374     0.48    0.93    0.90    0.93    0.40    1.00    
34000   -179.83 -22.47  5.60    26.34   -7.11   -4.84   -71.14  394     0.47    0.93    0.90    0.93    0.40    1.00    
35000   -178.03 -25.43  14.31   41.53   -6.15   -4.53   -86.83  391     0.47    0.93    0.90    0.93    0.40    1.00    
36000   -181.82 -23.85  8.41    35.59   -6.08   -5.25   -44.11  391     0.47    0.93    0.90    0.93    0.40    1.00    
37000   -179.72 -27.98  22.47   77.30   -6.17   -4.07   3.13    383     0.47    0.93    0.90    0.93    0.40    1.00    
38000   -179.67 -26.44  15.93   59.47   -5.96   -4.50   -30.31  403     0.47    0.93    0.90    0.93    0.40    1.00    
39000   -177.94 -23.52  7.91    32.23   -5.93   -4.50   0.86    380     0.47    0.93    0.90    0.93    0.40    1.00    
40000   -180.59 -23.09  7.30    28.20   -5.40   -4.64   80.16   403     0.47    0.93    0.90    0.93    0.40    1.00    
41000   -183.54 -24.52  11.30   35.50   -5.31   -4.13   51.20   426     0.47    0.93    0.90    0.93    0.40    1.00    
42000   -179.63 -24.34  10.41   35.69   -6.52   -4.58   -56.26  370     0.47    0.93    0.90    0.93    0.40    1.00    
43000   -177.41 -25.15  14.15   36.50   -6.11   -4.26   -22.26  404     0.47    0.93    0.90    0.93    0.40    1.00    
44000   -177.70 -24.00  9.03    35.58   -6.29   -4.53   -59.78  381     0.47    0.93    0.90    0.93    0.40    1.00    
45000   -179.94 -24.58  11.74   34.86   -5.80   -4.80   -7.82   394     0.47    0.93    0.90    0.93    0.40    1.00    
46000   -183.01 -24.93  11.66   41.83   -5.69   -4.16   7.71    407     0.47    0.93    0.90    0.93    0.41    1.00    
47000   -173.66 -24.58  12.76   31.52   -5.79   -4.26   -1.65   398     0.47    0.93    0.90    0.93    0.41    1.00    
48000   -175.61 -28.05  23.34   75.34   -6.39   -4.32   -28.94  389     0.47    0.93    0.90    0.93    0.41    1.00    
49000   -175.88 -23.43  7.46    32.53   -6.16   -4.64   -88.48  357     0.47    0.93    0.90    0.93    0.41    1.00    
50000   -179.32 -24.21  10.05   34.83   -5.98   -4.56   -29.40  426     0.47    0.93    0.90    0.93    0.41    1.00    
51000   -181.49 -24.65  10.91   39.46   -5.84   -4.18   12.50   383     0.47    0.93    0.90    0.93    0.41    1.00    
52000   -172.94 -25.75  14.39   48.27   -6.53   -4.77   -90.18  389     0.47    0.93    0.90    0.93    0.41    1.00    
53000   -183.86 -22.98  7.08    27.42   -6.61   -4.88   -40.08  391     0.47    0.93    0.90    0.93    0.41    1.00    
54000   -180.74 -23.91  10.59   28.26   -6.28   -4.10   -4.94   384     0.47    0.93    0.90    0.93    0.41    1.00    
55000   -183.33 -24.70  11.92   36.34   -5.73   -3.97   93.91   407     0.47    0.93    0.90    0.93    0.41    1.00    
56000   -178.05 -24.48  10.18   39.29   -5.50   -4.48   8.34    369     0.47    0.93    0.90    0.93    0.41    1.00    
57000   -178.65 -23.70  8.56    32.29   -6.03   -3.91   70.14   403     0.47    0.93    0.90    0.93    0.41    1.00    
58000   -173.95 -23.37  8.21    28.62   -5.60   -3.37   52.31   381     0.47    0.93    0.90    0.93    0.41    1.00    
59000   -181.67 -22.09  5.39    22.50   -6.07   -3.73   83.62   406     0.47    0.93    0.90    0.93    0.41    1.00    
60000   -172.09 -26.08  16.23   48.36   -6.65   -4.41   -76.53  369     0.47    0.93    0.90    0.93    0.41    1.00    
61000   -174.23 -23.36  8.82    26.48   -6.78   -4.48   -44.25  366     0.47    0.93    0.90    0.93    0.41    1.00    
62000   -180.06 -26.81  19.72   52.55   -5.88   -4.17   -0.57   357     0.47    0.93    0.90    0.93    0.41    1.00    
63000   -180.14 -24.18  9.61    36.22   -5.59   -4.29   62.08   390     0.47    0.93    0.90    0.93    0.41    1.00    
64000   -184.30 -26.12  18.26   41.64   -5.97   -3.68   4.83    384     0.47    0.93    0.90    0.93    0.41    1.00    
65000   -180.73 -24.13  9.14    37.33   -5.46   -4.67   -27.27  408     0.47    0.93    0.89    0.93    0.41    1.00    
66000   -183.62 -23.22  7.80    28.03   -6.47   -3.18   34.87   366     0.47    0.93    0.89    0.93    0.41    1.00    
67000   -176.80 -24.55  12.34   32.30   -6.10   -4.16   0.68    408     0.47    0.93    0.89    0.93    0.41    1.00    
68000   -177.00 -24.64  12.48   33.38   -5.72   -4.56   -2.47   357     0.47    0.93    0.89    0.93    0.41    1.00    
69000   -182.43 -22.75  5.56    30.55   -5.94   -4.79   -84.40  403     0.47    0.93    0.89    0.93    0.41    1.00    
70000   -173.31 -28.06  23.71   73.79   -6.42   -4.62   -71.27  404     0.47    0.93    0.89    0.93    0.41    1.00    
71000   -181.27 -22.27  5.56    24.05   -6.32   -4.83   12.58   380     0.47    0.93    0.89    0.93    0.41    1.00    
72000   -177.52 -25.15  12.34   43.57   -6.10   -4.99   -77.91  387     0.47    0.94    0.89    0.93    0.41    1.00    
73000   -178.40 -23.44  9.10    26.57   -6.30   -4.59   -56.17  352     0.47    0.94    0.90    0.93    0.41    1.00    
74000   -178.22 -24.88  11.10   43.51   -6.13   -5.05   -70.99  412     0.47    0.94    0.90    0.93    0.41    1.00    
75000   -174.84 -25.68  14.21   47.29   -6.26   -4.46   -56.01  396     0.47    0.94    0.90    0.93    0.41    1.00    
76000   -181.69 -25.26  10.83   53.97   -5.94   -4.56   12.89   352     0.47    0.94    0.90    0.93    0.41    1.00    
77000   -178.65 -23.94  9.32    33.32   -5.89   -5.05   -56.78  392     0.47    0.94    0.90    0.93    0.41    1.00    
78000   -180.42 -26.41  15.44   61.15   -6.16   -5.06   -29.35  366     0.47    0.94    0.90    0.93    0.41    1.00    
79000   -178.44 -23.30  8.95    25.22   -6.27   -5.02   -88.35  379     0.47    0.94    0.90    0.93    0.41    1.00    
80000   -176.94 -25.68  13.28   51.90   -6.10   -4.61   -65.19  383     0.47    0.94    0.90    0.93    0.41    1.00    
81000   -177.49 -25.70  14.69   45.76   -5.95   -4.83   -60.95  393     0.47    0.94    0.90    0.93    0.41    1.00    
82000   -179.53 -22.95  6.60    28.97   -6.80   -4.89   -87.57  404     0.47    0.94    0.90    0.93    0.41    1.00    
83000   -178.96 -25.09  12.90   40.10   -6.44   -4.66   -14.40  412     0.47    0.94    0.90    0.93    0.41    1.00    
84000   -172.35 -26.79  19.27   53.92   -6.35   -4.70   -81.40  394     0.47    0.94    0.90    0.93    0.41    1.00    
85000   -182.17 -22.27  5.57    23.97   -6.72   -4.16   -54.86  392     0.47    0.94    0.90    0.93    0.41    1.00    
86000   -179.01 -24.97  11.98   41.39   -5.96   -5.15   -34.18  399     0.47    0.94    0.90    0.93    0.41    1.00    
87000   -175.74 -23.80  9.87    29.02   -6.54   -4.77   -94.20  391     0.47    0.94    0.90    0.93    0.41    1.00    
88000   -179.49 -25.86  14.54   50.29   -5.92   -5.12   -71.84  404     0.47    0.94    0.90    0.93    0.41    1.00    
89000   -174.97 -25.66  13.99   47.98   -6.42   -4.52   -44.37  379     0.47    0.94    0.90    0.93    0.41    1.00    
90000   -179.57 -24.83  11.88   38.93   -6.20   -4.41   25.35   381     0.47    0.94    0.90    0.93    0.41    1.00    
91000   -179.47 -24.29  9.44    38.93   -5.90   -4.65   89.30   415     0.47    0.94    0.90    0.93    0.41    1.00    
92000   -180.13 -23.30  6.81    33.37   -6.01   -4.03   -0.48   390     0.47    0.94    0.90    0.93    0.41    1.00    
93000   -179.58 -27.28  20.83   61.13   -5.94   -4.76   -46.65  357     0.47    0.94    0.90    0.93    0.41    1.00    
94000   -179.80 -24.16  10.48   32.48   -6.49   -4.15   -81.58  352     0.47    0.94    0.90    0.93    0.41    1.00    
95000   -177.91 -23.94  8.95    34.82   -6.43   -5.20   -69.95  379     0.47    0.94    0.90    0.93    0.41    1.00    
96000   -174.04 -24.18  11.24   30.16   -6.37   -4.81   -79.04  408     0.47    0.94    0.90    0.93    0.41    1.00    
97000   -178.15 -23.79  8.78    32.96   -6.33   -4.79   -5.54   366     0.47    0.94    0.90    0.93    0.41    1.00    
98000   -181.99 -22.69  5.95    27.90   -5.34   -4.72   -8.72   404     0.47    0.94    0.90    0.93    0.41    1.00    
99000   -175.61 -23.08  7.02    29.07   -6.06   -3.91   11.24   407     0.47    0.94    0.90    0.93    0.41    1.00    
100000  -183.60 -21.18  3.90    18.25   -5.94   -3.13   -27.03  411     0.47    0.94    0.90    0.93    0.41    1.00    
chainD.step <- mymcmcD.step$load()
chainD.step <- set.burnin(chainD.step, 0.3)
plot(chainD.step)

strees.bayou <- EVtrees.bayou
mymcmcEV.sigmoid$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce beta_sl k       .alpha  .beta_c .beta_l .beta_r .beta_s .sig2   .theta  
1000    -381.94 -44.78  434.51  1320.44 -2.01   -5.52   86.13   0.01    343     0.47    0.83    0.84    0.85    0.73    0.39    1.00    
2000    -385.74 -39.95  217.74  756.84  -1.57   -6.82   80.25   0.01    369     0.38    0.77    0.80    0.85    0.59    0.36    1.00    
3000    -378.12 -38.91  206.87  533.67  -1.74   -6.66   66.78   0.01    314     0.34    0.75    0.79    0.84    0.54    0.35    1.00    
4000    -369.24 -44.48  432.56  1020.03 -1.43   -8.05   18.34   0.01    375     0.32    0.75    0.79    0.84    0.50    0.35    1.00    
5000    -384.94 -36.97  149.32  412.83  -0.99   -8.50   63.16   0.01    375     0.32    0.74    0.78    0.84    0.47    0.34    1.00    
6000    -366.57 -39.91  229.47  642.41  -1.04   -8.58   39.82   0.01    347     0.30    0.73    0.78    0.84    0.44    0.34    1.00    
7000    -383.51 -46.14  467.23  1541.03 -0.19   -8.85   98.80   0.01    336     0.30    0.72    0.77    0.84    0.42    0.33    1.00    
8000    -374.70 -42.44  298.88  874.87  -0.21   -9.49   78.04   0.01    364     0.31    0.71    0.76    0.84    0.40    0.32    1.00    
9000    -373.87 -40.79  241.31  783.22  -0.26   -10.16  39.10   0.01    347     0.31    0.72    0.76    0.84    0.38    0.32    1.00    
10000   -371.61 -44.54  378.26  1145.61 -0.21   -10.12  58.78   0.00    353     0.31    0.72    0.75    0.84    0.37    0.32    1.00    
11000   -378.59 -40.44  233.13  660.72  -0.52   -10.77  -1.15   0.01    344     0.31    0.71    0.75    0.85    0.36    0.32    1.00    
12000   -380.84 -46.72  490.15  1521.05 -0.67   -10.84  -34.50  0.00    364     0.30    0.71    0.75    0.85    0.36    0.31    1.00    
13000   -373.32 -42.69  317.55  742.27  -0.58   -10.44  16.78   0.00    348     0.30    0.71    0.75    0.85    0.35    0.31    1.00    
14000   -367.79 -44.31  376.06  1158.79 -0.13   -10.04  68.00   0.01    330     0.31    0.71    0.75    0.85    0.34    0.31    1.00    
15000   -370.96 -40.14  231.31  611.28  -0.55   -9.55   48.99   0.01    329     0.31    0.71    0.75    0.86    0.33    0.30    1.00    
16000   -375.12 -42.32  294.23  897.33  -1.18   -9.59   -12.04  0.01    338     0.31    0.71    0.75    0.86    0.33    0.30    1.00    
17000   -383.17 -36.13  123.94  387.67  -1.43   -9.64   -23.03  0.01    364     0.31    0.70    0.74    0.86    0.33    0.30    1.00    
18000   -377.07 -41.40  265.71  808.54  -1.61   -8.90   -14.00  0.01    375     0.31    0.71    0.75    0.86    0.33    0.30    1.00    
19000   -374.82 -38.16  178.75  464.33  -1.64   -8.44   2.99    0.01    342     0.31    0.70    0.75    0.86    0.33    0.30    1.00    
20000   -373.36 -40.48  243.97  713.56  -1.49   -8.17   17.81   0.01    334     0.31    0.70    0.75    0.86    0.33    0.30    1.00    
21000   -370.91 -41.89  304.73  810.19  -3.00   -7.55   -57.26  0.01    347     0.31    0.70    0.75    0.86    0.33    0.30    1.00    
22000   -376.60 -42.48  319.86  952.73  -3.07   -7.37   -63.78  0.01    375     0.31    0.70    0.75    0.86    0.33    0.30    1.00    
23000   -379.68 -39.51  210.06  656.10  -1.85   -7.75   9.35    0.01    375     0.31    0.70    0.75    0.86    0.33    0.30    1.00    
24000   -377.61 -41.62  285.88  826.90  -1.26   -7.21   79.59   0.01    375     0.31    0.69    0.75    0.86    0.33    0.30    1.00    
25000   -380.41 -41.28  272.84  717.47  -1.16   -8.01   37.46   0.01    323     0.31    0.69    0.75    0.86    0.33    0.30    1.00    
26000   -376.76 -43.34  352.88  1080.64 -0.96   -7.45   92.70   0.01    339     0.31    0.69    0.75    0.85    0.33    0.30    1.00    
27000   -376.33 -42.61  322.71  897.90  -1.64   -7.58   23.84   0.01    361     0.31    0.69    0.75    0.85    0.33    0.31    1.00    
28000   -386.15 -43.03  342.29  812.92  -2.34   -8.08   -39.18  0.01    321     0.31    0.69    0.75    0.85    0.33    0.31    1.00    
29000   -376.79 -41.15  266.32  741.19  -2.32   -7.70   -25.77  0.01    325     0.31    0.69    0.75    0.85    0.33    0.31    1.00    
30000   -388.41 -43.88  384.61  985.88  -2.63   -7.15   -35.71  0.01    310     0.30    0.69    0.76    0.85    0.34    0.31    1.00    
31000   -376.92 -48.99  685.50  1945.54 -2.63   -7.92   -67.75  0.01    344     0.31    0.68    0.76    0.85    0.34    0.31    1.00    
32000   -366.26 -45.32  469.32  1175.71 -2.70   -8.31   -69.93  0.01    368     0.31    0.69    0.76    0.86    0.34    0.31    1.00    
33000   -378.60 -38.94  199.95  582.00  -2.94   -8.03   -71.70  0.01    367     0.31    0.68    0.76    0.86    0.34    0.31    1.00    
34000   -373.92 -43.79  395.69  980.85  -2.70   -7.54   -42.63  0.01    353     0.31    0.68    0.76    0.86    0.34    0.31    1.00    
35000   -363.76 -42.98  337.45  932.52  -2.61   -8.85   -81.21  0.01    332     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
36000   -388.09 -39.78  206.63  664.83  -1.82   -9.30   -46.73  0.01    310     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
37000   -375.91 -43.81  358.60  1035.42 -1.70   -8.64   -11.89  0.01    360     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
38000   -378.57 -40.23  247.19  618.57  -2.54   -7.33   -36.81  0.01    364     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
39000   -369.74 -43.67  372.60  995.49  -2.92   -7.12   -52.95  0.01    332     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
40000   -374.96 -41.78  287.08  949.12  -3.51   -6.77   -64.98  0.01    339     0.30    0.69    0.76    0.86    0.33    0.31    1.00    
41000   -370.15 -40.56  258.74  632.09  -2.63   -7.23   -31.18  0.01    332     0.30    0.69    0.76    0.86    0.34    0.30    1.00    
42000   -372.65 -38.93  209.07  560.99  -2.85   -6.53   -10.56  0.01    327     0.30    0.69    0.76    0.86    0.34    0.30    1.00    
43000   -383.06 -45.44  448.36  1539.55 -2.82   -6.31   -4.07   0.01    333     0.30    0.69    0.76    0.86    0.34    0.30    1.00    
44000   -371.88 -37.40  170.56  455.52  -2.42   -5.71   47.97   0.01    332     0.30    0.69    0.76    0.86    0.35    0.30    1.00    
45000   -376.17 -40.87  270.89  694.75  -3.25   -6.38   -40.17  0.01    360     0.30    0.69    0.76    0.86    0.35    0.30    1.00    
46000   -373.97 -41.95  307.28  858.50  -3.60   -5.45   -40.75  0.01    343     0.30    0.69    0.75    0.86    0.35    0.30    1.00    
47000   -380.72 -39.98  237.09  719.98  -3.87   -4.48   -19.18  0.02    352     0.30    0.69    0.76    0.86    0.36    0.30    1.00    
48000   -384.12 -39.43  215.56  702.13  -4.32   -3.38   -7.92   0.02    351     0.30    0.69    0.76    0.87    0.37    0.30    1.00    
49000   -379.65 -38.70  203.94  566.72  -4.65   -3.48   -42.85  0.02    320     0.30    0.69    0.76    0.87    0.38    0.30    1.00    
50000   -371.17 -40.06  231.70  758.87  -4.53   -3.22   -29.57  0.03    368     0.30    0.69    0.76    0.87    0.39    0.30    1.00    
51000   -382.73 -41.27  276.20  895.22  -4.68   -3.18   -30.31  0.02    367     0.30    0.69    0.76    0.87    0.40    0.30    1.00    
52000   -368.42 -40.45  259.97  692.32  -4.31   -2.86   11.49   0.02    376     0.30    0.69    0.76    0.87    0.40    0.30    1.00    
53000   -371.85 -45.88  496.40  1599.39 -4.58   -3.76   -48.66  0.02    327     0.30    0.69    0.76    0.87    0.41    0.30    1.00    
54000   -360.76 -45.86  516.50  1401.02 -3.99   -4.33   -17.25  0.02    348     0.30    0.69    0.76    0.87    0.42    0.30    1.00    
55000   -375.62 -43.18  353.74  1144.17 -4.11   -3.67   -12.50  0.02    342     0.30    0.69    0.76    0.87    0.42    0.30    1.00    
56000   -359.55 -40.87  276.21  736.04  -4.53   -3.63   -41.55  0.02    375     0.30    0.69    0.76    0.87    0.43    0.30    1.00    
57000   -372.44 -37.10  166.01  428.95  -4.64   -3.12   -21.01  0.02    348     0.30    0.69    0.76    0.87    0.44    0.30    1.00    
58000   -372.11 -39.11  214.33  614.19  -4.17   -3.46   -6.37   0.02    346     0.30    0.69    0.76    0.87    0.44    0.30    1.00    
59000   -381.21 -40.39  244.27  773.60  -4.26   -3.06   1.70    0.03    313     0.30    0.68    0.76    0.87    0.45    0.30    1.00    
60000   -378.88 -38.93  209.30  569.08  -4.42   -2.85   8.91    0.03    350     0.30    0.68    0.75    0.87    0.46    0.30    1.00    
61000   -376.40 -41.93  307.76  889.65  -4.54   -3.08   -11.80  0.03    338     0.30    0.68    0.76    0.87    0.47    0.30    1.00    
62000   -376.76 -42.03  305.54  918.96  -4.59   -2.92   -21.48  0.03    364     0.30    0.68    0.76    0.87    0.47    0.30    1.00    
63000   -379.17 -37.02  144.72  534.15  -4.58   -3.23   -15.82  0.04    353     0.30    0.68    0.76    0.87    0.48    0.30    1.00    
64000   -378.55 -41.48  288.36  882.06  -4.34   -3.28   -0.25   0.02    336     0.30    0.68    0.76    0.86    0.49    0.30    1.00    
65000   -362.49 -38.50  203.85  518.52  -4.13   -4.11   -23.33  0.02    346     0.30    0.68    0.76    0.86    0.49    0.30    1.00    
66000   -365.48 -38.40  190.69  586.21  -4.13   -3.86   -10.61  0.02    371     0.30    0.68    0.76    0.87    0.49    0.30    1.00    
67000   -364.32 -40.99  282.51  735.18  -4.24   -4.22   -38.53  0.02    348     0.30    0.68    0.76    0.86    0.50    0.30    1.00    
68000   -382.53 -40.71  251.97  880.98  -3.99   -4.12   -15.81  0.02    336     0.30    0.68    0.76    0.86    0.50    0.30    1.00    
69000   -382.21 -38.53  204.16  507.18  -3.74   -4.72   -7.78   0.01    391     0.30    0.68    0.76    0.87    0.51    0.30    1.00    
70000   -383.41 -36.89  155.45  457.46  -3.67   -4.69   0.80    0.01    352     0.30    0.68    0.76    0.87    0.51    0.30    1.00    
71000   -380.85 -42.06  314.00  913.77  -4.19   -4.04   -36.08  0.01    364     0.30    0.68    0.76    0.87    0.51    0.30    1.00    
72000   -373.59 -40.62  269.69  691.31  -4.03   -3.64   -6.41   0.02    358     0.30    0.68    0.76    0.87    0.51    0.30    1.00    
73000   -379.07 -39.96  237.56  701.38  -3.65   -4.49   -12.05  0.01    320     0.30    0.68    0.75    0.87    0.52    0.30    1.00    
74000   -377.66 -43.77  395.69  1065.70 -3.73   -4.53   -4.12   0.01    363     0.30    0.68    0.76    0.87    0.52    0.30    1.00    
75000   -370.45 -40.04  242.32  645.39  -3.43   -6.11   -43.30  0.01    347     0.30    0.68    0.76    0.87    0.52    0.30    1.00    
76000   -377.27 -47.05  567.97  1690.90 -3.53   -6.41   -63.57  0.01    363     0.30    0.68    0.75    0.87    0.52    0.30    1.00    
77000   -366.02 -43.33  374.21  924.31  -3.57   -6.38   -65.51  0.01    342     0.30    0.68    0.75    0.87    0.52    0.30    1.00    
78000   -360.47 -43.30  367.16  1058.68 -3.94   -4.72   -32.27  0.01    371     0.30    0.69    0.75    0.87    0.52    0.30    1.00    
79000   -381.72 -45.23  478.07  1215.66 -4.00   -4.79   -45.06  0.01    351     0.30    0.68    0.75    0.87    0.52    0.30    1.00    
80000   -376.96 -42.57  331.11  1030.76 -4.06   -4.38   -32.17  0.02    313     0.30    0.68    0.75    0.87    0.52    0.30    1.00    
81000   -373.18 -43.17  358.77  1081.93 -4.39   -4.14   -44.53  0.02    323     0.30    0.69    0.75    0.87    0.53    0.30    1.00    
82000   -373.30 -42.37  327.67  904.87  -4.65   -3.11   -25.25  0.03    332     0.30    0.69    0.76    0.87    0.53    0.30    1.00    
83000   -365.89 -44.86  468.57  1138.34 -4.36   -3.66   -17.71  0.02    348     0.30    0.69    0.75    0.87    0.54    0.30    1.00    
84000   -373.96 -38.94  200.81  646.39  -4.31   -3.82   -15.32  0.03    327     0.30    0.69    0.75    0.87    0.54    0.30    1.00    
85000   -375.05 -45.22  481.19  1249.34 -4.40   -4.01   -40.60  0.02    320     0.30    0.69    0.75    0.87    0.54    0.30    1.00    
86000   -370.66 -40.28  254.21  682.40  -4.52   -3.08   -27.38  0.02    348     0.30    0.69    0.75    0.87    0.54    0.30    1.00    
87000   -380.16 -44.26  403.60  1332.99 -4.00   -3.99   -9.59   0.02    352     0.30    0.69    0.76    0.87    0.55    0.30    1.00    
88000   -372.67 -44.12  413.88  1111.28 -3.95   -4.51   -31.28  0.01    353     0.30    0.69    0.76    0.87    0.55    0.30    1.00    
89000   -362.97 -42.44  343.69  860.74  -3.83   -4.30   -19.00  0.02    334     0.30    0.69    0.76    0.87    0.55    0.30    1.00    
90000   -364.90 -40.98  274.37  789.07  -4.60   -3.65   -31.37  0.02    349     0.30    0.69    0.75    0.87    0.55    0.30    1.00    
91000   -386.33 -37.88  180.07  525.91  -4.36   -3.81   -22.89  0.02    364     0.30    0.68    0.76    0.87    0.55    0.30    1.00    
92000   -371.46 -40.02  253.15  612.96  -4.27   -4.28   -39.28  0.02    342     0.30    0.69    0.75    0.87    0.56    0.30    1.00    
93000   -376.24 -40.88  278.62  723.87  -4.43   -3.46   -29.90  0.02    360     0.30    0.69    0.76    0.87    0.56    0.30    1.00    
94000   -376.88 -41.13  283.18  754.37  -4.82   -2.89   -30.53  0.03    350     0.30    0.68    0.76    0.87    0.56    0.30    1.00    
95000   -379.49 -38.59  207.14  517.65  -4.34   -4.05   -30.63  0.02    360     0.30    0.68    0.76    0.87    0.57    0.30    1.00    
96000   -378.34 -40.07  247.62  664.86  -4.00   -3.86   -9.18   0.02    347     0.30    0.68    0.76    0.87    0.57    0.30    1.00    
97000   -377.78 -40.73  264.90  767.02  -4.42   -3.99   -34.07  0.02    363     0.30    0.68    0.75    0.87    0.57    0.30    1.00    
98000   -372.09 -43.61  404.30  943.27  -4.30   -3.93   -31.73  0.02    314     0.30    0.68    0.75    0.87    0.57    0.30    1.00    
99000   -382.51 -37.84  172.64  575.76  -4.07   -4.01   -19.07  0.02    378     0.30    0.68    0.75    0.87    0.57    0.30    1.00    
100000  -362.49 -40.93  284.79  697.20  -3.71   -4.56   -12.90  0.02    370     0.30    0.68    0.75    0.87    0.57    0.30    1.00    
chainEV.sigmoid <- mymcmcEV.sigmoid$load()
chainEV.sigmoid <- set.burnin(chainEV.sigmoid, 0.3)
plot(chainEV.sigmoid)

strees.bayou <- Dtrees.bayou
mymcmcD.sigmoid$run(100000)
gen     lnL     prior   alpha   sig2    beta_le beta_ri beta_ce beta_sl k       .alpha  .beta_c .beta_l .beta_r .beta_s .sig2   .theta  
1000    -187.80 -20.54  7.28    26.75   -0.99   -5.43   74.58   0.03    352     0.55    0.86    0.91    0.94    0.92    0.44    1.00    
2000    -181.37 -20.44  5.93    32.81   -1.43   -4.70   68.02   0.02    402     0.49    0.85    0.92    0.93    0.91    0.44    1.00    
3000    -175.73 -21.87  10.60   36.31   -1.71   -4.80   57.79   0.01    379     0.51    0.80    0.91    0.93    0.91    0.43    1.00    
4000    -173.96 -22.08  12.31   30.61   -1.45   -5.50   63.03   0.01    412     0.52    0.81    0.90    0.92    0.88    0.42    1.00    
5000    -176.10 -23.07  14.29   42.29   -1.10   -5.84   67.11   0.01    406     0.52    0.82    0.89    0.91    0.83    0.42    1.00    
6000    -175.99 -22.90  12.23   38.43   -1.04   -6.38   79.77   0.01    370     0.50    0.82    0.88    0.91    0.79    0.41    1.00    
7000    -179.85 -22.56  11.58   37.98   -1.38   -6.89   -17.55  0.01    395     0.51    0.83    0.88    0.91    0.76    0.41    1.00    
8000    -172.07 -21.85  10.49   30.60   -1.70   -6.41   22.94   0.01    409     0.50    0.84    0.88    0.91    0.74    0.41    1.00    
9000    -177.79 -22.77  11.40   35.47   -2.21   -5.91   -22.42  0.01    369     0.50    0.84    0.88    0.91    0.72    0.42    1.00    
10000   -169.58 -24.14  16.34   45.72   -2.72   -5.34   -52.81  0.01    369     0.49    0.85    0.88    0.91    0.71    0.42    1.00    
11000   -178.01 -21.53  8.10    34.70   -2.93   -5.08   -66.78  0.01    370     0.49    0.85    0.88    0.91    0.71    0.42    1.00    
12000   -178.95 -22.94  11.40   48.84   -2.85   -5.12   -74.25  0.01    392     0.49    0.86    0.88    0.91    0.71    0.42    1.00    
13000   -182.57 -21.06  8.36    30.95   -3.67   -4.12   -73.31  0.01    391     0.48    0.86    0.88    0.91    0.71    0.41    1.00    
14000   -178.21 -23.26  12.84   38.76   -3.23   -4.88   -70.62  0.01    425     0.48    0.86    0.88    0.91    0.72    0.40    1.00    
15000   -173.31 -22.27  11.35   33.88   -2.95   -4.95   -70.59  0.01    435     0.48    0.86    0.87    0.92    0.72    0.41    1.00    
16000   -173.31 -23.78  15.03   48.36   -2.69   -4.88   -44.70  0.01    379     0.49    0.86    0.87    0.92    0.72    0.40    1.00    
17000   -176.70 -23.14  10.78   32.63   -2.86   -5.51   -85.88  0.00    387     0.48    0.86    0.87    0.92    0.71    0.40    1.00    
18000   -167.64 -23.86  16.33   46.26   -2.11   -6.19   -38.10  0.01    398     0.48    0.86    0.87    0.92    0.71    0.41    1.00    
19000   -177.76 -24.18  14.73   53.16   -2.91   -6.14   -96.11  0.01    381     0.48    0.86    0.87    0.92    0.70    0.40    1.00    
20000   -173.50 -21.50  8.60    30.19   -2.85   -5.62   -77.27  0.01    387     0.48    0.86    0.87    0.92    0.70    0.41    1.00    
21000   -179.92 -21.30  6.56    26.38   -2.49   -5.33   22.24   0.00    386     0.48    0.86    0.87    0.92    0.69    0.41    1.00    
22000   -172.45 -23.21  14.21   39.94   -2.16   -4.86   46.30   0.01    357     0.48    0.86    0.87    0.92    0.69    0.41    1.00    
23000   -179.89 -22.62  9.21    34.79   -2.10   -5.61   38.06   0.00    407     0.48    0.86    0.87    0.92    0.69    0.41    1.00    
24000   -175.21 -24.95  17.59   65.58   -1.41   -5.89   87.57   0.01    404     0.48    0.86    0.87    0.92    0.69    0.41    1.00    
25000   -172.50 -22.92  13.52   32.79   -2.15   -5.65   18.62   0.01    381     0.47    0.86    0.87    0.92    0.69    0.41    1.00    
26000   -174.52 -21.02  9.70    26.79   -1.76   -4.70   98.46   0.02    409     0.47    0.86    0.87    0.92    0.69    0.41    1.00    
27000   -173.34 -23.32  17.21   40.14   -1.92   -4.48   94.30   0.01    424     0.47    0.86    0.87    0.92    0.69    0.41    1.00    
28000   -170.67 -21.89  10.82   36.09   -2.28   -4.23   62.49   0.01    394     0.47    0.86    0.87    0.92    0.69    0.41    1.00    
29000   -175.72 -22.14  11.27   36.20   -2.87   -4.26   -12.11  0.01    406     0.47    0.86    0.87    0.92    0.70    0.41    1.00    
30000   -176.88 -20.66  6.82    25.70   -3.64   -3.91   -65.24  0.01    402     0.47    0.86    0.87    0.92    0.70    0.41    1.00    
31000   -175.26 -24.43  19.43   58.88   -3.72   -3.21   -36.89  0.02    398     0.48    0.86    0.87    0.92    0.71    0.41    1.00    
32000   -175.75 -25.71  24.57   72.73   -4.04   -2.38   -41.71  0.01    382     0.48    0.86    0.87    0.92    0.71    0.41    1.00    
33000   -179.95 -19.67  6.09    21.05   -4.45   -2.11   -29.14  0.01    402     0.48    0.86    0.87    0.92    0.72    0.41    1.00    
34000   -176.69 -21.65  10.08   34.89   -4.24   -2.47   -86.59  0.02    408     0.48    0.86    0.87    0.92    0.72    0.41    1.00    
35000   -177.21 -21.73  12.26   28.87   -3.85   -3.33   -65.66  0.02    384     0.48    0.86    0.87    0.92    0.73    0.41    1.00    
36000   -176.47 -22.04  12.43   31.92   -3.42   -3.74   -40.22  0.01    398     0.48    0.86    0.87    0.92    0.73    0.41    1.00    
37000   -178.10 -20.49  8.00    25.36   -4.23   -3.27   -94.87  0.02    383     0.48    0.86    0.87    0.92    0.73    0.41    1.00    
38000   -175.82 -21.80  9.63    37.97   -4.27   -2.94   -33.67  0.03    373     0.48    0.86    0.87    0.92    0.74    0.41    1.00    
39000   -182.61 -20.93  9.00    25.25   -3.96   -1.84   5.74    0.03    381     0.47    0.86    0.87    0.92    0.74    0.41    1.00    
40000   -177.90 -22.44  12.75   38.82   -4.57   -1.42   8.69    0.02    406     0.48    0.86    0.87    0.92    0.75    0.41    1.00    
41000   -178.81 -21.02  7.54    31.99   -3.30   -2.82   36.12   0.01    398     0.48    0.87    0.87    0.92    0.75    0.41    1.00    
42000   -169.39 -21.64  10.09   32.44   -3.40   -2.72   21.06   0.03    388     0.48    0.87    0.87    0.92    0.76    0.41    1.00    
43000   -179.56 -20.59  7.16    29.67   -4.17   -2.27   -71.33  0.02    386     0.48    0.87    0.87    0.91    0.76    0.41    1.00    
44000   -171.05 -24.07  17.71   54.51   -4.07   -2.69   -31.01  0.01    379     0.48    0.87    0.87    0.91    0.77    0.41    1.00    
45000   -177.61 -23.22  14.67   43.13   -4.35   -1.69   -13.68  0.01    399     0.48    0.87    0.87    0.91    0.77    0.41    1.00    
46000   -179.09 -22.56  12.45   41.55   -3.91   -2.35   -39.00  0.03    420     0.48    0.87    0.87    0.91    0.77    0.41    1.00    
47000   -175.31 -22.15  11.93   36.72   -4.33   -2.48   -98.10  0.02    383     0.47    0.87    0.87    0.91    0.78    0.41    1.00    
48000   -176.26 -23.26  16.27   38.07   -3.53   -3.12   2.03    0.01    406     0.47    0.87    0.87    0.91    0.78    0.41    1.00    
49000   -177.20 -20.78  8.26    26.28   -4.08   -2.91   -53.23  0.01    392     0.47    0.87    0.87    0.92    0.78    0.41    1.00    
50000   -174.13 -21.55  9.89    34.04   -3.16   -3.17   2.35    0.02    424     0.47    0.87    0.87    0.92    0.78    0.41    1.00    
51000   -180.35 -21.11  7.15    35.58   -3.71   -3.46   -70.67  0.01    403     0.47    0.87    0.87    0.91    0.78    0.41    1.00    
52000   -174.74 -22.96  12.60   39.16   -3.18   -4.55   -77.09  0.01    352     0.47    0.87    0.87    0.92    0.79    0.41    1.00    
53000   -177.42 -20.85  8.42    26.06   -3.41   -3.40   8.36    0.01    392     0.48    0.87    0.87    0.91    0.79    0.41    1.00    
54000   -177.54 -23.17  14.10   49.48   -4.01   -2.41   13.26   0.02    370     0.47    0.87    0.87    0.91    0.79    0.41    1.00    
55000   -179.46 -20.77  8.50    26.87   -4.21   -2.79   -71.10  0.02    390     0.47    0.87    0.87    0.91    0.79    0.41    1.00    
56000   -172.75 -24.40  21.62   48.76   -4.46   -2.04   -62.51  0.02    406     0.47    0.87    0.87    0.91    0.79    0.41    1.00    
57000   -176.01 -28.61  42.50   106.79  -4.34   -2.03   -56.37  0.04    408     0.47    0.87    0.87    0.91    0.80    0.41    1.00    
58000   -178.40 -26.55  30.86   72.05   -3.87   -2.22   -0.07   0.04    391     0.48    0.87    0.87    0.91    0.80    0.41    1.00    
59000   -169.90 -22.59  13.58   37.54   -3.98   -2.48   -42.59  0.03    360     0.48    0.87    0.87    0.91    0.80    0.41    1.00    
60000   -170.88 -24.27  20.41   49.33   -3.84   -2.84   -19.00  0.01    373     0.47    0.87    0.87    0.91    0.80    0.41    1.00    
61000   -168.31 -23.34  18.05   38.30   -3.92   -2.72   -24.42  0.02    398     0.47    0.87    0.87    0.91    0.80    0.41    1.00    
62000   -172.46 -26.22  28.38   79.84   -3.74   -3.36   -65.67  0.02    395     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
63000   -175.80 -22.50  11.70   38.47   -3.60   -3.62   -32.84  0.01    403     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
64000   -179.54 -23.08  14.37   42.07   -3.95   -2.80   -41.80  0.01    391     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
65000   -174.25 -21.92  10.74   37.27   -3.95   -2.79   -47.47  0.02    437     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
66000   -179.34 -20.14  5.75    24.07   -4.11   -2.41   -22.33  0.04    396     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
67000   -177.59 -25.23  20.62   57.23   -4.73   -2.18   -96.67  0.06    368     0.47    0.87    0.87    0.91    0.81    0.41    1.00    
68000   -176.14 -24.10  16.85   37.94   -3.73   -2.00   19.81   0.07    388     0.47    0.87    0.87    0.91    0.82    0.41    1.00    
69000   -172.37 -23.90  14.63   36.46   -4.11   -1.78   -17.61  0.09    360     0.47    0.87    0.87    0.91    0.82    0.41    1.00    
70000   -179.17 -22.68  8.17    32.60   -4.06   -1.90   -35.89  0.11    386     0.47    0.87    0.87    0.91    0.82    0.41    1.00    
71000   -174.69 -23.88  10.93   37.90   -4.76   -1.70   -92.56  0.13    415     0.47    0.87    0.87    0.91    0.82    0.41    1.00    
72000   -174.99 -26.34  18.23   47.51   -4.46   -2.12   -77.35  0.17    384     0.47    0.87    0.87    0.91    0.83    0.41    1.00    
73000   -179.07 -23.37  8.29    35.11   -3.90   -2.05   16.34   0.15    408     0.47    0.87    0.87    0.91    0.83    0.41    1.00    
74000   -179.74 -23.26  7.56    34.47   -3.86   -1.84   84.80   0.15    415     0.47    0.87    0.87    0.91    0.83    0.41    1.00    
75000   -176.60 -24.86  12.68   46.87   -4.24   -1.62   76.95   0.14    388     0.47    0.87    0.87    0.91    0.83    0.41    1.00    
76000   -179.16 -24.79  12.21   44.33   -4.21   -1.79   59.65   0.15    420     0.47    0.87    0.87    0.91    0.83    0.41    1.00    
77000   -176.77 -23.86  10.04   38.97   -4.94   -1.53   -89.77  0.14    372     0.47    0.87    0.87    0.91    0.84    0.41    1.00    
78000   -177.98 -24.39  11.36   37.32   -4.86   -1.84   -85.18  0.16    425     0.47    0.87    0.87    0.91    0.84    0.41    1.00    
79000   -179.28 -25.62  13.57   51.23   -4.82   -1.27   -41.03  0.17    386     0.47    0.87    0.87    0.91    0.84    0.41    1.00    
80000   -169.34 -25.05  16.70   41.51   -4.44   -1.47   -33.89  0.12    406     0.47    0.87    0.87    0.91    0.84    0.41    1.00    
81000   -174.14 -24.97  14.75   39.06   -4.64   -2.31   -90.69  0.15    383     0.47    0.87    0.87    0.91    0.84    0.41    1.00    
82000   -173.97 -25.19  16.43   40.99   -4.64   -1.43   -75.79  0.13    404     0.47    0.87    0.87    0.91    0.85    0.41    1.00    
83000   -178.64 -25.36  16.87   45.27   -5.03   -1.24   -35.80  0.13    381     0.47    0.87    0.87    0.91    0.85    0.41    1.00    
84000   -181.50 -21.63  7.54    23.15   -4.93   -1.29   -55.76  0.10    368     0.47    0.87    0.87    0.91    0.85    0.41    1.00    
85000   -178.57 -22.21  7.23    29.82   -4.90   -1.03   16.26   0.11    411     0.47    0.87    0.87    0.91    0.85    0.41    1.00    
86000   -180.93 -23.05  9.74    32.46   -4.96   -0.71   -95.12  0.11    392     0.47    0.87    0.87    0.91    0.85    0.41    1.00    
87000   -177.47 -22.23  8.45    29.48   -4.83   -1.24   -27.34  0.09    366     0.47    0.88    0.87    0.91    0.85    0.41    1.00    
88000   -179.32 -22.10  9.34    28.33   -3.90   -2.42   2.58    0.08    437     0.47    0.88    0.87    0.91    0.85    0.41    1.00    
89000   -179.70 -21.91  7.94    35.35   -5.06   -1.78   -86.55  0.06    399     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
90000   -178.87 -21.19  6.80    23.69   -5.14   -1.17   -59.60  0.08    400     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
91000   -179.25 -21.67  7.50    27.80   -4.57   -1.43   -61.86  0.08    387     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
92000   -178.81 -22.12  6.35    30.40   -5.25   -1.35   -54.38  0.12    390     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
93000   -175.21 -24.36  12.69   36.54   -4.14   -1.87   -10.89  0.14    392     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
94000   -176.99 -24.51  11.50   47.70   -4.27   -1.95   -20.92  0.13    398     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
95000   -177.31 -23.96  9.96    38.29   -4.49   -1.83   21.87   0.15    372     0.47    0.88    0.87    0.91    0.86    0.41    1.00    
96000   -174.08 -26.88  20.24   60.30   -4.21   -1.98   17.57   0.16    366     0.47    0.88    0.87    0.91    0.87    0.41    1.00    
97000   -178.95 -23.97  9.82    33.18   -3.89   -1.81   2.44    0.17    386     0.47    0.88    0.87    0.91    0.87    0.41    1.00    
98000   -179.14 -24.44  9.68    38.19   -4.91   -1.62   -79.79  0.19    403     0.47    0.88    0.87    0.91    0.87    0.41    1.00    
99000   -178.56 -25.81  13.96   52.65   -4.69   -1.55   -70.47  0.17    366     0.47    0.88    0.87    0.91    0.87    0.41    1.00    
100000  -172.91 -23.74  9.55    30.21   -4.19   -2.49   -81.92  0.17    375     0.47    0.88    0.87    0.91    0.87    0.41    1.00    
chainD.sigmoid <- mymcmcD.sigmoid$load()
chainD.sigmoid <- set.burnin(chainD.sigmoid, 0.3)
plot(chainD.sigmoid)

strees.bayou <- EVtrees.bayou
mymcmcEV.linear$run(100000)
gen     lnL     prior   alpha   sig2    beta_in beta_sl k       .alpha  .beta_i .beta_s .sig2   .theta  
1000    -375.69 -32.60  257.03  687.85  -5.79   0.01    307     0.41    0.63    0.67    0.35    1.00    
2000    -366.95 -32.65  259.55  693.37  -6.00   0.01    375     0.37    0.55    0.60    0.35    1.00    
3000    -370.39 -35.98  384.68  1173.67 -5.85   0.01    348     0.35    0.54    0.58    0.30    1.00    
4000    -379.18 -33.03  263.89  797.83  -6.00   0.01    353     0.34    0.52    0.55    0.29    1.00    
5000    -369.36 -34.32  314.83  922.01  -5.84   0.01    347     0.33    0.51    0.55    0.29    1.00    
6000    -377.09 -31.86  235.75  604.47  -5.83   0.01    323     0.33    0.51    0.55    0.30    1.00    
7000    -366.39 -37.61  485.26  1313.04 -5.86   0.01    325     0.32    0.51    0.55    0.30    1.00    
8000    -381.82 -31.29  208.55  635.44  -5.99   0.01    367     0.31    0.50    0.55    0.30    1.00    
9000    -378.95 -29.93  174.32  514.12  -5.87   0.01    323     0.31    0.50    0.55    0.30    1.00    
10000   -370.47 -34.77  333.27  980.12  -5.87   0.01    348     0.31    0.50    0.55    0.31    1.00    
11000   -368.83 -35.64  387.70  969.08  -6.01   0.01    349     0.30    0.50    0.54    0.31    1.00    
12000   -380.82 -30.08  179.91  512.32  -5.98   0.01    347     0.30    0.50    0.54    0.30    1.00    
13000   -381.53 -33.43  276.08  857.68  -5.97   0.01    367     0.30    0.50    0.54    0.31    1.00    
14000   -373.77 -35.85  395.21  1019.63 -5.92   0.01    357     0.30    0.49    0.54    0.31    1.00    
15000   -371.95 -30.93  206.70  541.97  -5.84   0.01    368     0.30    0.49    0.54    0.30    1.00    
16000   -383.06 -32.76  257.61  746.48  -5.88   0.01    351     0.30    0.49    0.54    0.30    1.00    
17000   -375.04 -34.95  333.10  1073.25 -5.81   0.01    323     0.30    0.49    0.54    0.30    1.00    
18000   -377.12 -30.51  188.54  559.75  -5.85   0.01    369     0.31    0.49    0.53    0.30    1.00    
19000   -369.89 -37.44  462.38  1396.61 -5.83   0.01    332     0.30    0.49    0.53    0.30    1.00    
20000   -382.44 -28.34  140.40  402.00  -6.02   0.01    358     0.30    0.49    0.53    0.30    1.00    
21000   -379.90 -31.46  221.08  592.00  -5.85   0.01    378     0.30    0.49    0.53    0.30    1.00    
22000   -376.02 -31.30  218.71  560.95  -5.89   0.01    360     0.30    0.48    0.53    0.30    1.00    
23000   -374.49 -36.92  441.28  1243.68 -6.04   0.01    338     0.30    0.49    0.53    0.30    1.00    
24000   -375.98 -34.67  335.43  911.70  -5.84   0.01    367     0.30    0.49    0.53    0.30    1.00    
25000   -372.27 -30.53  182.06  621.86  -5.96   0.01    332     0.30    0.49    0.53    0.30    1.00    
26000   -368.04 -33.34  282.10  770.80  -5.88   0.01    314     0.30    0.49    0.53    0.30    1.00    
27000   -368.68 -39.44  609.56  1595.81 -5.80   0.01    332     0.30    0.49    0.53    0.30    1.00    
28000   -366.47 -33.90  301.38  847.67  -5.87   0.01    347     0.30    0.49    0.52    0.30    1.00    
29000   -373.25 -37.09  451.38  1263.30 -5.94   0.01    357     0.30    0.49    0.52    0.30    1.00    
30000   -389.32 -31.05  198.31  643.29  -5.92   0.01    310     0.30    0.49    0.52    0.30    1.00    
31000   -362.78 -35.10  348.89  1011.10 -5.94   0.01    311     0.30    0.49    0.52    0.30    1.00    
32000   -368.14 -33.12  287.81  651.10  -5.81   0.01    334     0.30    0.49    0.52    0.30    1.00    
33000   -379.40 -33.66  275.14  972.39  -5.91   0.01    347     0.30    0.49    0.52    0.30    1.00    
34000   -374.01 -32.37  236.45  777.70  -5.88   0.01    368     0.30    0.49    0.52    0.30    1.00    
35000   -371.21 -31.57  219.12  639.09  -5.87   0.01    330     0.30    0.49    0.52    0.30    1.00    
36000   -380.42 -34.11  295.82  995.19  -5.96   0.01    375     0.30    0.49    0.52    0.30    1.00    
37000   -375.00 -31.24  204.49  652.18  -5.90   0.01    347     0.30    0.49    0.52    0.30    1.00    
38000   -374.40 -32.50  265.40  599.71  -5.77   0.01    368     0.30    0.49    0.52    0.30    1.00    
39000   -382.89 -29.01  145.64  511.42  -5.85   0.01    357     0.30    0.49    0.52    0.30    1.00    
40000   -367.62 -35.21  362.79  951.39  -6.05   0.01    342     0.30    0.48    0.52    0.30    1.00    
41000   -382.53 -31.72  220.40  675.94  -5.72   0.01    336     0.30    0.48    0.52    0.30    1.00    
42000   -382.23 -31.78  225.07  662.61  -6.09   0.01    336     0.30    0.48    0.53    0.30    1.00    
43000   -377.17 -33.00  273.90  704.71  -5.82   0.01    357     0.30    0.49    0.53    0.30    1.00    
44000   -375.87 -29.80  172.61  495.82  -5.88   0.01    350     0.30    0.48    0.53    0.30    1.00    
45000   -374.75 -32.52  256.73  666.34  -5.87   0.01    350     0.30    0.48    0.53    0.30    1.00    
46000   -373.71 -34.25  328.44  787.57  -6.07   0.01    330     0.30    0.48    0.53    0.30    1.00    
47000   -374.73 -35.95  383.05  1171.61 -5.99   0.01    350     0.30    0.48    0.53    0.30    1.00    
48000   -367.66 -36.22  401.77  1159.16 -5.78   0.01    348     0.30    0.48    0.53    0.30    1.00    
49000   -369.72 -33.54  294.28  754.66  -5.87   0.01    347     0.30    0.48    0.53    0.30    1.00    
50000   -382.87 -32.50  249.68  711.46  -5.87   0.01    363     0.30    0.48    0.53    0.30    1.00    
51000   -384.55 -34.71  339.83  898.56  -5.89   0.01    313     0.30    0.48    0.53    0.30    1.00    
52000   -378.58 -30.57  186.96  592.08  -5.90   0.01    314     0.30    0.48    0.53    0.30    1.00    
53000   -379.79 -30.89  197.31  600.22  -5.88   0.01    314     0.30    0.48    0.53    0.30    1.00    
54000   -374.31 -33.60  280.72  891.50  -6.00   0.01    358     0.30    0.48    0.53    0.30    1.00    
55000   -384.84 -34.71  333.45  948.97  -5.93   0.01    333     0.30    0.48    0.53    0.30    1.00    
56000   -371.33 -31.87  232.07  636.28  -5.83   0.01    347     0.30    0.48    0.53    0.30    1.00    
57000   -378.38 -30.27  184.82  520.77  -5.75   0.01    327     0.30    0.48    0.53    0.30    1.00    
58000   -375.30 -30.05  172.49  561.55  -6.06   0.01    325     0.30    0.48    0.53    0.30    1.00    
59000   -366.62 -38.39  519.00  1570.93 -5.81   0.01    365     0.30    0.48    0.53    0.30    1.00    
60000   -367.25 -35.17  366.86  900.51  -5.91   0.01    348     0.30    0.48    0.53    0.30    1.00    
61000   -380.20 -36.12  389.95  1198.36 -5.72   0.01    353     0.30    0.48    0.53    0.30    1.00    
62000   -377.69 -34.17  314.07  861.45  -5.96   0.01    353     0.30    0.48    0.53    0.30    1.00    
63000   -380.99 -35.02  348.89  967.98  -5.79   0.01    326     0.30    0.48    0.53    0.30    1.00    
64000   -373.84 -33.48  281.60  828.20  -5.74   0.01    329     0.30    0.48    0.53    0.30    1.00    
65000   -372.40 -38.43  540.16  1418.66 -5.86   0.01    364     0.30    0.48    0.53    0.30    1.00    
66000   -384.31 -31.04  199.78  625.14  -5.73   0.01    350     0.30    0.48    0.53    0.30    1.00    
67000   -375.68 -32.03  229.40  712.58  -5.97   0.01    348     0.30    0.48    0.53    0.30    1.00    
68000   -379.97 -32.51  241.54  786.40  -5.92   0.01    347     0.30    0.48    0.53    0.30    1.00    
69000   -374.66 -34.73  332.71  961.36  -5.79   0.01    314     0.30    0.48    0.53    0.30    1.00    
70000   -379.11 -36.26  399.58  1208.48 -5.92   0.01    367     0.30    0.48    0.53    0.30    1.00    
71000   -373.87 -33.84  300.11  831.56  -5.95   0.01    358     0.30    0.48    0.53    0.30    1.00    
72000   -384.95 -31.35  211.75  628.34  -5.84   0.01    342     0.30    0.48    0.53    0.30    1.00    
73000   -377.22 -32.50  248.54  723.25  -5.89   0.01    357     0.30    0.48    0.53    0.30    1.00    
74000   -378.22 -33.14  280.06  715.08  -5.91   0.01    378     0.30    0.48    0.53    0.30    1.00    
75000   -367.93 -36.18  391.33  1238.00 -5.91   0.01    346     0.30    0.48    0.53    0.30    1.00    
76000   -386.79 -32.04  221.68  779.72  -5.78   0.01    336     0.30    0.48    0.53    0.30    1.00    
77000   -370.74 -31.18  219.70  522.45  -5.86   0.01    348     0.30    0.48    0.53    0.30    1.00    
78000   -366.94 -42.26  863.07  2087.46 -5.88   0.01    314     0.30    0.48    0.53    0.30    1.00    
79000   -380.22 -36.06  405.41  1046.60 -5.84   0.01    320     0.30    0.48    0.53    0.30    1.00    
80000   -374.16 -32.58  253.40  709.34  -5.77   0.01    324     0.30    0.48    0.53    0.30    1.00    
81000   -381.50 -34.18  320.51  817.15  -5.92   0.01    336     0.30    0.48    0.53    0.30    1.00    
82000   -383.02 -31.47  209.96  683.45  -6.05   0.01    367     0.30    0.48    0.53    0.30    1.00    
83000   -373.13 -33.12  268.86  792.02  -5.92   0.01    376     0.30    0.48    0.53    0.30    1.00    
84000   -376.62 -31.29  209.92  623.25  -5.88   0.01    369     0.30    0.48    0.53    0.30    1.00    
85000   -372.13 -30.72  202.01  521.35  -5.97   0.01    349     0.30    0.48    0.53    0.30    1.00    
86000   -374.03 -34.24  330.26  771.61  -5.96   0.01    317     0.30    0.48    0.53    0.30    1.00    
87000   -375.82 -33.25  278.00  766.59  -5.77   0.01    314     0.30    0.48    0.53    0.30    1.00    
88000   -376.60 -32.75  259.24  729.83  -5.89   0.01    361     0.30    0.48    0.53    0.30    1.00    
89000   -380.50 -32.88  260.44  767.08  -5.95   0.01    348     0.30    0.48    0.53    0.30    1.00    
90000   -386.14 -30.97  210.42  528.42  -5.84   0.01    367     0.30    0.48    0.53    0.30    1.00    
91000   -385.05 -34.13  312.48  856.09  -5.93   0.01    336     0.30    0.48    0.53    0.30    1.00    
92000   -387.15 -29.50  164.41  481.90  -5.84   0.01    348     0.30    0.48    0.53    0.30    1.00    
93000   -374.98 -36.95  435.42  1316.14 -5.91   0.01    369     0.30    0.48    0.53    0.30    1.00    
94000   -382.91 -30.54  190.69  551.82  -5.97   0.01    375     0.30    0.48    0.53    0.30    1.00    
95000   -370.75 -30.28  186.87  511.70  -5.95   0.01    348     0.30    0.48    0.53    0.30    1.00    
96000   -376.11 -33.27  264.92  888.97  -6.11   0.01    351     0.30    0.48    0.53    0.30    1.00    
97000   -381.73 -31.46  223.78  572.28  -5.94   0.01    378     0.30    0.48    0.53    0.30    1.00    
98000   -383.43 -34.52  326.61  916.56  -5.96   0.01    321     0.30    0.48    0.53    0.30    1.00    
99000   -373.88 -35.73  380.98  1065.74 -5.88   0.01    314     0.30    0.48    0.53    0.30    1.00    
100000  -376.88 -31.83  233.21  615.43  -5.89   0.01    350     0.30    0.48    0.53    0.30    1.00    
chainEV.linear <- mymcmcEV.linear$load()
chainEV.linear <- set.burnin(chainEV.linear, 0.3)
plot(chainEV.linear)

strees.bayou <- Dtrees.bayou
mymcmcD.linear$run(100000)
gen     lnL     prior   alpha   sig2    beta_in beta_sl k       .alpha  .beta_i .beta_s .sig2   .theta  
1000    -174.14 -14.71  15.34   29.65   -5.38   0.01    388     0.50    0.73    0.65    0.44    1.00    
2000    -173.79 -15.04  12.30   42.92   -4.93   0.01    435     0.46    0.70    0.63    0.40    1.00    
3000    -172.51 -14.35  11.52   34.19   -5.09   0.01    372     0.45    0.70    0.65    0.39    1.00    
4000    -171.20 -14.95  13.76   36.00   -4.99   0.01    424     0.45    0.69    0.64    0.38    1.00    
5000    -176.05 -15.29  13.41   43.56   -4.93   0.01    385     0.46    0.68    0.63    0.37    1.00    
6000    -178.46 -12.94  6.76    25.68   -4.51   0.01    406     0.47    0.68    0.63    0.37    1.00    
7000    -175.65 -14.74  12.02   41.38   -5.38   0.01    380     0.46    0.68    0.64    0.38    1.00    
8000    -175.56 -13.69  10.04   28.28   -4.98   0.01    437     0.46    0.68    0.64    0.38    1.00    
9000    -178.13 -14.42  10.25   41.08   -5.15   0.01    381     0.45    0.68    0.64    0.38    1.00    
10000   -173.51 -14.37  11.57   34.91   -5.20   0.01    369     0.45    0.69    0.65    0.39    1.00    
11000   -172.69 -14.83  12.70   37.46   -4.98   0.01    402     0.45    0.69    0.65    0.39    1.00    
12000   -174.88 -15.28  13.12   45.92   -5.06   0.01    380     0.45    0.69    0.66    0.39    1.00    
13000   -175.76 -14.94  13.38   36.81   -4.94   0.01    372     0.45    0.69    0.66    0.39    1.00    
14000   -177.03 -14.01  10.74   29.81   -4.87   0.01    383     0.45    0.68    0.66    0.39    1.00    
15000   -172.72 -19.82  34.32   103.64  -5.11   0.01    412     0.46    0.68    0.66    0.39    1.00    
16000   -181.03 -15.23  14.56   36.86   -4.83   0.01    399     0.46    0.68    0.66    0.39    1.00    
17000   -178.67 -16.31  16.12   58.66   -5.09   0.01    366     0.46    0.69    0.65    0.39    1.00    
18000   -179.81 -12.82  6.30    24.16   -4.31   0.01    404     0.46    0.69    0.66    0.40    1.00    
19000   -175.07 -15.46  15.35   39.91   -4.97   0.01    379     0.46    0.69    0.66    0.40    1.00    
20000   -177.94 -13.81  9.71    32.18   -5.13   0.01    369     0.46    0.69    0.66    0.40    1.00    
21000   -175.49 -15.10  13.64   39.73   -5.05   0.01    396     0.46    0.69    0.66    0.40    1.00    
22000   -166.27 -17.32  24.68   52.39   -5.20   0.01    388     0.46    0.69    0.66    0.39    1.00    
23000   -182.43 -17.01  18.43   63.12   -4.73   0.01    391     0.46    0.69    0.66    0.40    1.00    
24000   -171.77 -18.86  33.63   67.93   -5.25   0.01    391     0.46    0.69    0.66    0.40    1.00    
25000   -177.61 -14.75  11.03   44.84   -5.20   0.01    387     0.46    0.69    0.66    0.40    1.00    
26000   -176.94 -12.73  7.85    23.36   -5.08   0.01    398     0.46    0.69    0.66    0.40    1.00    
27000   -174.22 -18.00  25.87   67.89   -5.16   0.01    380     0.46    0.69    0.66    0.40    1.00    
28000   -175.38 -14.81  12.59   38.35   -5.06   0.01    395     0.46    0.69    0.66    0.40    1.00    
29000   -179.72 -15.68  15.08   49.16   -5.33   0.00    407     0.46    0.69    0.66    0.40    1.00    
30000   -176.36 -14.80  12.18   41.26   -5.27   0.01    409     0.46    0.69    0.66    0.40    1.00    
31000   -172.62 -15.33  13.78   45.17   -5.18   0.01    404     0.46    0.69    0.66    0.40    1.00    
32000   -171.11 -15.47  15.33   41.58   -5.12   0.01    366     0.46    0.69    0.66    0.40    1.00    
33000   -174.75 -14.26  9.72    38.54   -4.93   0.01    402     0.46    0.69    0.66    0.40    1.00    
34000   -175.51 -20.72  39.62   117.07  -4.82   0.01    387     0.46    0.69    0.66    0.40    1.00    
35000   -178.46 -12.84  7.76    25.57   -5.21   0.01    370     0.47    0.69    0.66    0.40    1.00    
36000   -175.46 -13.60  8.00    34.72   -5.00   0.01    372     0.47    0.69    0.66    0.40    1.00    
37000   -174.86 -15.11  12.61   45.05   -5.14   0.01    396     0.47    0.69    0.66    0.40    1.00    
38000   -179.89 -12.95  7.76    27.74   -5.38   0.00    403     0.47    0.69    0.66    0.40    1.00    
39000   -177.68 -13.89  9.53    31.47   -4.78   0.01    387     0.47    0.69    0.66    0.40    1.00    
40000   -179.80 -13.18  9.28    23.82   -4.95   0.01    374     0.47    0.69    0.66    0.40    1.00    
41000   -175.59 -14.32  11.19   32.27   -4.77   0.01    404     0.46    0.69    0.66    0.40    1.00    
42000   -177.91 -18.77  30.69   77.14   -5.34   0.01    392     0.46    0.69    0.66    0.40    1.00    
43000   -176.86 -13.94  9.31    34.64   -4.96   0.01    383     0.46    0.69    0.66    0.40    1.00    
44000   -178.40 -15.13  13.53   43.31   -5.39   0.01    399     0.46    0.69    0.66    0.40    1.00    
45000   -168.49 -15.13  14.34   37.01   -4.96   0.01    366     0.46    0.69    0.66    0.40    1.00    
46000   -174.16 -16.95  20.30   58.24   -5.12   0.01    368     0.46    0.69    0.66    0.40    1.00    
47000   -175.05 -15.31  14.18   43.73   -5.27   0.01    406     0.46    0.69    0.66    0.40    1.00    
48000   -175.41 -16.64  18.63   54.79   -4.95   0.01    415     0.46    0.69    0.66    0.40    1.00    
49000   -175.81 -14.03  10.14   34.81   -5.24   0.01    415     0.46    0.69    0.66    0.40    1.00    
50000   -177.80 -13.23  7.41    32.29   -5.15   0.01    408     0.46    0.69    0.66    0.40    1.00    
51000   -174.49 -14.98  13.61   37.65   -5.04   0.01    437     0.46    0.69    0.66    0.40    1.00    
52000   -176.22 -14.71  11.91   39.92   -5.17   0.01    387     0.46    0.69    0.66    0.40    1.00    
53000   -173.55 -14.42  12.43   32.53   -5.14   0.01    403     0.46    0.69    0.66    0.40    1.00    
54000   -178.08 -16.07  18.76   41.57   -5.06   0.01    372     0.46    0.69    0.66    0.40    1.00    
55000   -176.59 -13.84  9.26    35.08   -5.24   0.01    411     0.46    0.69    0.66    0.40    1.00    
56000   -171.58 -17.88  24.61   66.40   -4.95   0.01    372     0.46    0.69    0.66    0.40    1.00    
57000   -174.79 -14.48  10.46   35.49   -4.55   0.01    408     0.46    0.69    0.66    0.40    1.00    
58000   -180.79 -14.02  9.07    38.49   -5.11   0.01    407     0.46    0.69    0.66    0.40    1.00    
59000   -178.81 -14.82  12.12   39.86   -5.01   0.01    369     0.46    0.69    0.66    0.40    1.00    
60000   -173.84 -14.19  9.79    36.69   -4.91   0.01    399     0.47    0.69    0.66    0.40    1.00    
61000   -176.48 -15.29  13.10   48.30   -5.29   0.01    374     0.47    0.69    0.66    0.40    1.00    
62000   -176.46 -16.18  16.75   53.07   -5.18   0.01    385     0.47    0.69    0.66    0.40    1.00    
63000   -173.85 -15.69  14.10   46.71   -4.69   0.01    380     0.47    0.69    0.66    0.40    1.00    
64000   -169.73 -15.74  17.02   39.74   -4.96   0.01    388     0.47    0.69    0.66    0.40    1.00    
65000   -177.04 -15.29  12.69   44.76   -4.77   0.01    393     0.47    0.69    0.66    0.40    1.00    
66000   -177.82 -15.95  17.01   47.05   -5.26   0.01    381     0.46    0.69    0.66    0.40    1.00    
67000   -179.80 -14.46  11.31   37.61   -5.19   0.00    426     0.47    0.69    0.66    0.40    1.00    
68000   -178.54 -14.49  11.53   37.63   -5.24   0.00    423     0.47    0.69    0.66    0.40    1.00    
69000   -173.12 -15.14  14.39   36.58   -4.90   0.01    379     0.47    0.69    0.66    0.40    1.00    
70000   -176.81 -15.27  13.30   45.06   -5.08   0.01    399     0.47    0.69    0.66    0.40    1.00    
71000   -169.43 -16.99  20.11   56.87   -4.86   0.01    383     0.46    0.69    0.66    0.40    1.00    
72000   -178.33 -17.24  20.58   64.82   -5.03   0.01    423     0.46    0.69    0.65    0.40    1.00    
73000   -176.30 -13.17  8.31    27.26   -5.03   0.01    395     0.47    0.69    0.65    0.40    1.00    
74000   -175.09 -18.53  28.39   73.29   -4.99   0.01    368     0.46    0.69    0.65    0.40    1.00    
75000   -169.79 -16.89  21.07   50.26   -4.85   0.01    423     0.46    0.69    0.65    0.40    1.00    
76000   -175.25 -14.14  9.93    36.13   -5.01   0.01    352     0.46    0.69    0.65    0.40    1.00    
77000   -171.12 -16.08  18.38   41.30   -4.86   0.01    395     0.46    0.69    0.65    0.40    1.00    
78000   -174.80 -13.35  9.13    26.07   -4.91   0.01    404     0.46    0.69    0.65    0.40    1.00    
79000   -179.75 -13.30  8.21    31.52   -5.47   0.01    404     0.46    0.69    0.65    0.40    1.00    
80000   -172.72 -17.58  23.02   61.27   -4.82   0.01    372     0.47    0.69    0.65    0.40    1.00    
81000   -173.91 -15.31  15.78   32.93   -4.67   0.01    388     0.47    0.69    0.65    0.40    1.00    
82000   -172.69 -15.66  14.91   43.81   -4.78   0.01    395     0.47    0.69    0.65    0.40    1.00    
83000   -175.70 -15.96  15.35   53.87   -5.19   0.01    420     0.47    0.69    0.65    0.40    1.00    
84000   -171.35 -15.44  14.90   43.00   -5.16   0.01    408     0.47    0.69    0.65    0.40    1.00    
85000   -173.64 -15.67  14.90   46.30   -4.97   0.01    396     0.47    0.69    0.65    0.40    1.00    
86000   -178.07 -15.79  15.28   50.06   -5.24   0.01    411     0.47    0.69    0.65    0.40    1.00    
87000   -180.07 -12.44  6.66    23.81   -5.09   0.01    399     0.47    0.69    0.65    0.40    1.00    
88000   -174.67 -18.98  26.84   98.73   -4.91   0.01    415     0.47    0.69    0.65    0.40    1.00    
89000   -176.27 -15.24  14.38   38.91   -4.95   0.01    385     0.47    0.69    0.65    0.40    1.00    
90000   -174.45 -16.36  17.52   52.90   -5.03   0.01    391     0.47    0.69    0.65    0.40    1.00    
91000   -171.66 -16.12  17.17   45.22   -4.78   0.01    381     0.47    0.68    0.65    0.40    1.00    
92000   -172.61 -13.25  8.22    28.63   -5.05   0.01    390     0.47    0.68    0.65    0.40    1.00    
93000   -184.38 -12.07  4.54    23.08   -4.45   0.01    391     0.47    0.68    0.65    0.40    1.00    
94000   -176.00 -13.45  7.99    33.05   -5.12   0.01    404     0.47    0.68    0.65    0.41    1.00    
95000   -178.27 -14.75  11.77   42.98   -5.43   0.01    391     0.47    0.69    0.65    0.41    1.00    
96000   -169.50 -14.74  11.60   38.79   -4.84   0.01    394     0.47    0.69    0.65    0.41    1.00    
97000   -176.12 -15.31  13.79   42.61   -4.95   0.01    392     0.46    0.68    0.65    0.41    1.00    
98000   -167.91 -15.73  16.69   38.65   -4.76   0.01    366     0.47    0.68    0.65    0.40    1.00    
99000   -176.85 -15.34  12.79   45.61   -4.77   0.01    406     0.47    0.68    0.65    0.40    1.00    
100000  -174.30 -13.25  8.37    28.71   -5.14   0.01    382     0.47    0.68    0.65    0.40    1.00    
chainD.linear <- mymcmcD.linear$load()
chainD.linear <- set.burnin(chainD.linear, 0.3)
plot(chainD.linear)

optimizeModelsOverSimmap <- function(i, strees.bayou=strees.bayou, chain.sigmoid=chain.sigmoid, chain.linear=chain.linear, chain.step=chain.step, cache = cache){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
require(optimx)
cacheEV <- bayou:::.prepare.ou.univariate(tdEV$phy, tdEV[['lnVs']], SE=0.02, pred=tdEV['binpred'])
cacheD <- bayou:::.prepare.ou.univariate(tdD$phy, tdD[['lnVs']], SE=0.02, pred=tdD['binpred'])
EVopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=EVtrees.bayou, chain.sigmoid = chainEV.sigmoid, chain.step=chainEV.step, chain.linear=chainEV.linear, cache = cacheEV)
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Dopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=Dtrees.bayou, chain.sigmoid = chainD.sigmoid, chain.step=chainD.step, chain.linear=chainD.linear, cache = cacheD)
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
summarizeAllOpt <- function(opt.allmodels, cutoff=-Inf){
  all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
  all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
  all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
  all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
  all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
  all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
  all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
  all.aics[all.aics < cutoff] <- NA
  boxplot(all.aics)
  all.aics <- as.data.frame(all.aics)
  t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
  hist(all.aics$aic.linear - all.aics$aic.sigmoid)
  tmp <- reshape2::melt(all.aics)
  aicws <- aicw(tmp$value)$w
  aicws <- round(tapply(aicws, tmp$variable, sum),2)
  return(list(sigmoid=all.sigmoid, step=all.step, sigmoid0=all.sigmoid0, step0 = all.step0, linear=all.linear, tanhLin=all.tanhLin, aics=all.aics, aicws=aicws))
}
sumEV <- summarizeAllOpt(EVopt.allmodels)

No id variables; using all as measure variables

sumD <- summarizeAllOpt(Dopt.allmodels)

No id variables; using all as measure variables

sumEV$aicws
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
        0.58         0.00         0.06         0.33         0.00         0.03 
sumD$aicws
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
        0.27         0.01         0.38         0.26         0.02         0.07 
dAICs <- apply(sumEV$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV")

boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV", ylim=c(0,20))

cat("Mean EV dAIC values\n")
Mean EV dAIC values
apply(t(dAICs), 2, mean)
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
   0.6305072   43.1810208    3.6929463    1.1235774   41.1810208    4.6331318 
dAICs <- apply(sumD$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="D")

boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC",main="D", ylim=c(0,8))

cat("Mean Deciduous dAIC values\n")
Mean Deciduous dAIC values
apply(t(dAICs), 2, mean)
 aic.sigmoid     aic.step   aic.linear aic.sigmoid0    aic.step0  aic.tanhLin 
   3.8844676    9.3537055    0.5131179    1.7110603    7.3537055    3.3364835 
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21)
x <- -600:200
abline(v=0)
dum <- lapply(1:nrow(sumEV$sigmoid), function(i) lines(x, sumEV$sigmoid[i, 3] + sumEV$sigmoid[i, 4]/(1 + exp(sumEV$sigmoid[i, 6]*(x-sumEV$sigmoid[i, 5]))), col="green", lty=1))
dum <- lapply(1:nrow(sumEV$sigmoid0), function(i) lines(x, sumEV$sigmoid0[i, 3] + sumEV$sigmoid0[i, 4]/(1 + exp(sumEV$sigmoid0[i, 5]*(x-0))), col="limegreen", lty=1))
#dum <- lapply(1:nrow(sumEV$tanhLin), function(i) lines(x, sumEV$tanhLin[i, 3] + sumEV$tanhLin[i,6]*x + sumEV$tanhLin[i,4]*tanh(x-sumEV$tanhLin[i,5]), col="green", lty=1))
dum <- lapply(1:nrow(sumD$linear), function(i) lines(x, sumD$linear[i,3] + sumD$linear[i,4]*x, col="orange2", lty=1))
dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i, 4]/(1 + exp(sumD$sigmoid0[i, 5]*(x-0))), col="orange", lty=1))

#dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i,4]/(1+exp(sumD$sigmoid0[i,5]*x)), col="tan", lty=1))
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21, xlab = "Temp (Cx10)", ylab="Log Vessel Diameter")
x <- -400:200
abline(v=0)
dum <- lapply(seq(1, length(chainEV.sigmoid$gen), 1), function(i) lines(x, chainEV.sigmoid$beta_left[i]+chainEV.sigmoid$beta_right[i]/(1 + exp(chainEV.sigmoid$beta_slope[i]*(x-chainEV.sigmoid$beta_center[i]))), col=makeTransparent(rgb(0, 1, 0), alpha=10)))
dum <- lapply(seq(1, length(chainD.linear$gen), 1), function(i) lines(x, chainD.linear$beta_intercept[i]+chainD.linear$beta_slope[i]*x, col=makeTransparent(rgb(0.9, 0.5, 0.2), alpha=10)))

---
title: "Vessel size and continuous predictors"
output: html_notebook
---

An experimental method to deal with continuous predictors and possible alternative functional relationships between them and the trait of interest. In this case, we are modeling vessel size in response to environmental temperature. 

```{r}
## Load in libraries
library(bayou)
library(treeplyr)
library(diversitree)
library(optimx)

## Set working directory
setwd("~/repos/growthforms/analysis")

```


```{r}
td <- readRDS("../output/cleandata/tdOUwie.rds")
td
```


Bayesian update functions:

```{r}
## Test 3 different functions. 
## 1. Step function with 3 parameters:
.updateStepFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- ifelse(midbins>pars.new$beta_center, pars.new$beta_right, pars.new$beta_left)
  return(list(pars=pars.new, hr=hr))
}

## 2. Sigmoid function with 5 parameters
.updateSigmoidFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_left + pars.new$beta_right/(1 + exp(pars.new$beta_slope * (midbins - pars.new$beta_center)))
  return(list(pars=pars.new, hr=hr))
}

## 3. Linear function with 2 parameters
.updateLinearFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
  pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
  hr <- 0
  pars.new$theta <- pars.new$beta_intercept + pars.new$beta_slope * midbins
  return(list(pars=pars.new, hr=hr))
}
```

Bayesian update functions for dealing with simmap reconstructions
```{r}
## Other needed functions
.newMap <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  stree <- reorderSimmap(make.simmap(cache$phy, setNames(cache$pred[[1]], cache$phy$tip.label), Q=Q, message=FALSE), "postorder")
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  pars.new$sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}

.newMapPresample <- function(pars, cache, d, ct, prior=NULL, move=NULL){
  pars.new <- pars
  index <- sample(1:length(strees.bayou), 1, replace=FALSE)
  pars.new$sb <- strees.bayou[[index]]$sb #unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  pars.new$t2 <- strees.bayou[[index]]$t2 #as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  pars.new$loc <- unname(strees.bayou[[index]]$loc) #unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  pars.new$k <- length(pars.new$sb)
  hr <- 0
  return(list(pars=pars.new, hr=Inf))
}

## Empty functions to replace the deterministically specified theta values
dnull <- function(x, log=TRUE){
  if(log) 0 else 1
}

rnull <- function(x){
  return(x)
}
```


Place the predictor into bins and transform the tree to unit height.
```{r}
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])


td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat

```


Fit a meristic model to the binned predictor data and construct a set of stochastic maps. 

```{r}
## Use diversitree to fit a meristic model:
mkn.lik <- make.mkn.meristic(td$phy, td[['binpred']], k = ncat)
mkn.lik <- constrain(mkn.lik, q.up~q.down)
p <- c(0.1)
fit.mkn <- find.mle(mkn.lik, p)
Q <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q[i, i+1] <- fit.mkn$par[1]
  Q[i+1, i] <- fit.mkn$par[1]
}
diag(Q) <- apply(Q, 1, sum)*-1
rownames(Q) <- colnames(Q) <- 1:ncol(Q)
simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
td <- reorder(td, "postorder")
stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees <- make.simmap(td$phy, td[['binpred']], Q=Q, nsim=100)
for(i in 1:length(strees)) strees[[i]] <- reorderSimmap(strees[[i]], "postorder")
strees.bayou <- lapply(strees, simmap2bayou)
plotSimmap(stree, col=setNames(cm.colors(ncat), 1:ncat))


model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik

shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unname(unlist(sapply(stree$maps, function(x) cumsum(x)[-1])))
tmp <- lm(td[[trait]]~td[[pred]]+I(td[[pred]]^2)+I(td[[pred]]^3))

saveRDS(strees.bayou, "../output/OUwie/strees.bayou.rds")

```

```{r}
strees.bayou <- readRDS("../output/OUwie/strees.bayou.rds")
```


```{r}
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.step <- list(alpha=1, sig2=0.5, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.step$theta)


sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoid <- list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.sigmoid$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linear <- list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.linear$theta)

```


```{r}
## Set priors
prior.step <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)

prior.sigmoid <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)

prior.linear <- make.prior(td$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
```



```{r}
model.stepTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                beta_left=".updateStepFxTheta",
                                beta_right=".updateStepFxTheta",
                                beta_center=".updateStepFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.stepTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```


```{r}
model.sigmoidTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_left=".updateSigmoidFxTheta",
                                beta_right=".updateSigmoidFxTheta",
                                beta_center=".updateSigmoidFxTheta",
                                beta_slope=".updateSigmoidFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_left=10,
                                          beta_right=10,
                                          beta_center=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_left=0.2,
                            beta_right=0.2,
                            beta_center=20,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.sigmoidTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```

```{r}
model.linearTheta <- list(moves = list(alpha=".multiplierProposal", 
                                      sig2=".multiplierProposal",
                                  beta_intercept=".updateLinearFxTheta",
                                beta_slope=".updateLinearFxTheta",
                                theta=".newMapPresample"),
                   
                   control.weights = list(alpha=10, sig2=10,
                                          beta_intercept=10,
                                          beta_slope=10,
                                          theta=1,
                                          k=0),
                   D = list(alpha=1, sig2=1, 
                            beta_intercept=0.5,
                            beta_slope=0.005,
                            theta = 1),
                   parorder = c("alpha", "sig2", "beta_intercept", "beta_slope", "theta", "k", "ntheta"),
                   rjpars = c(),
                   shiftpars = c("sb", "loc", "t2"),
                   lik.fn = bayou.lik)

model.linearTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
  names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_intercept", "beta_slope", "k") #The parameters we want to print
  string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
  acceptratios <- tapply(accept, accept.type, mean)
  names <- c(names, names(acceptratios))
  if(j==0){
    cat(sprintf("%-7.7s", names), "\n", sep=" ")                           
  }
  cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_intercept, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}

```


```{r}
## Just a bunch of tests

cache <- bayou:::.prepare.ou.univariate(td$phy, td[['lnVs']], SE=0, pred=td['binpred'])
prior.step(startpar.step)
model.stepTheta$lik.fn(startpar.step, cache, cache$dat)$loglik
ct <- bayou:::.buildControl(startpar.step, prior.step, move.weights=model.stepTheta$control.weights)
pars.new <- .newMapPresample(startpar.step, cache, d=model.stepTheta$D$theta, ct = ct)$pars
prior.step(pars.new)
model.stepTheta$lik.fn(pars.new, cache, cache$dat)$loglik
pp <- pars.new
prior.step(pp)
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik

new.pp <- .newMapPresample(pp, cache, 1, ct)$pars
model.stepTheta$lik.fn(new.pp, cache, cache$dat)$loglik

prior.linear(startpar.linear)
new.pp <- .newMapPresample(startpar.linear, cache, 1, ct)$pars
model.linearTheta$lik.fn(new.pp, cache, cache$dat)$loglik




```


```{r}

mymcmc.step <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.stepTheta, prior=prior.step, startpar=startpar.step, new.dir=TRUE, outname="stepTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)


mymcmc.sigmoid <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoid, startpar=startpar.sigmoid, new.dir=TRUE, outname="sigmoidTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)


mymcmc.linear <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linear, startpar=startpar.linear, new.dir=TRUE, outname="linearTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)

```


```{r}
mymcmc.step$run(100000)
chain.step <- mymcmc.step$load()
chain.step <- set.burnin(chain.step, 0.3)
plot(chain.step)
```


```{r}
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.step$gen), 10), function(i) lines(x, ifelse(x>chain.step$beta_center[i], chain.step$beta_right[i], chain.step$beta_left[i]), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```

```{r}
mymcmc.sigmoid$run(100000)
chain.sigmoid <- mymcmc.sigmoid$load()
chain.sigmoid <- set.burnin(chain.sigmoid, 0.3)
plot(chain.sigmoid)
```


```{r}
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.sigmoid$gen), 1), function(i) lines(x, chain.sigmoid$beta_left[i]+chain.sigmoid$beta_right[i]/(1 + exp(chain.sigmoid$beta_slope[i]*(x-chain.sigmoid$beta_center[i]))), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```



```{r}
mymcmc.linear$run(100000)
chain.linear <- mymcmc.linear$load()
chain.linear <- set.burnin(chain.linear, 0.3)
plot(chain.linear)
```


```{r}
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.linear$gen), 1), function(i) lines(x, chain.linear$beta_intercept[i]+chain.linear$beta_slope[i]*x, col=makeTransparent(rgb(1, 0, 0), alpha=10)))
```




```{r}
plot(chain.step$lnL, type="n", ylim=c(-700, -500))
lines(1:length(chain.sigmoid$lnL), chain.sigmoid$lnL, col="green")
lines(1:length(chain.linear$lnL), chain.linear$lnL, col="blue")
lines(1:length(chain.step$lnL), chain.step$lnL, col="red")
saveRDS(chain.sigmoid, "../output/OUwie/chain.sigmoid.rds")
saveRDS(chain.linear, "../output/OUwie/chain.linear.rds")
saveRDS(chain.step, "../output/OUwie/chain.step.rds")
chain.sigmoid <- readRDS("../output/OUwie/chain.sigmoid.rds")
chain.linear <- readRDS("../output/OUwie/chain.linear.rds")
chain.step <- readRDS("../output/OUwie/chain.step.rds")

```

Let's do a likelihood based analysis. 

```{r}
library(optimx)
i <- 10
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

```


```{r}
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), upper=c(1000, 1000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(1000, 1000, 0, 0, 200))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(1000, 1000, 0, 1))

```

```{r}
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics
```

```{r}
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
Vy.linear <- sqrt(opt.linear$p2/(2*opt.linear$p1))
lines(x, opt.linear$p3+opt.linear$p4*x, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) + 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) - 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)

Vy.sigmoid <- opt.sigmoid$p2/(2*opt.sigmoid$p1)
lines(x, opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5))), col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) + 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) - 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)

Vy.step <- opt.step$p2/(2*opt.step$p1)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3), col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) + 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) - 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)

```


```{r}
## Script it over all trees. 
optimizeModelsOverSimmap <- function(i){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

pp.sigmoid0 <- pp.sigmoid[-5]

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))

opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))

opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))

aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics

return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}


```


```{r}

opt.allmodels <- lapply(1:100, optimizeModelsOverSimmap)

```

```{r}
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
tmp <- reshape2::melt(all.aics)
cat("Akaike weights\n")
aicws <- aicw(tmp$value)$w
round(tapply(aicws, tmp$variable, sum),2)

dAICs <- apply(all.aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", ylim=c(0,8))
cat("Mean dAIC values\n")
apply(t(dAICs), 2, mean)

```

# Independent analyses for Deciduous vs. Evergreen
Let's filter by phenology and analyze each independently. This is not ideal, since transitions occur between deciduous and evergreen species, and are not accounted for when we filter this way. However, if phylogenetic half-lives are small, this is probably of negligible importance. 

```{r}
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
bins

td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat

```



```{r}
tdEV <- filter(td, phenology == "EV")
tdD <- filter(td, phenology == "D")
tdEV$phy <- reorder(tdEV$phy, "postorder")
tdD$phy <- reorder(tdD$phy, "postorder")
#tdEV$phy$edge.length <- tdEV$phy$edge.length/max(branching.times(tdEV$phy))*100

## Use diversitree to fit a meristic model:
mknEV.lik <- make.mkn.meristic(tdEV$phy, tdEV[['binpred']], k = ncat)
mknD.lik <- make.mkn.meristic(tdD$phy, tdD[['binpred']], k = ncat)
mknEV.lik <- constrain(mknEV.lik, q.up~q.down)
mknD.lik <- constrain(mknD.lik, q.up~q.down)
p <- c(0.1)
EVfit.mkn <- find.mle(mknEV.lik, p)
Dfit.mkn <- find.mle(mknD.lik, p)

Q.EV <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.EV[i, i+1] <- EVfit.mkn$par[1]
  Q.EV[i+1, i] <- EVfit.mkn$par[1]
}

Q.D <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
  Q.D[i, i+1] <- Dfit.mkn$par[1]
  Q.D[i+1, i] <- Dfit.mkn$par[1]
}

diag(Q.EV) <- apply(Q.EV, 1, sum)*-1
diag(Q.D) <- apply(Q.D, 1, sum)*-1

rownames(Q.EV) <- colnames(Q.EV) <- 1:ncol(Q.EV)
rownames(Q.D) <- colnames(Q.D) <- 1:ncol(Q.D)

simmap2bayou <- function(stree){
  shifts <- sapply(stree$maps, function(x) names(x)[-1])
  sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
  t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
  loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
  return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
#stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees.EV <- make.simmap(tdEV$phy, tdEV[['binpred']], Q=Q.EV, nsim=100)
strees.D <- make.simmap(tdD$phy, tdD[['binpred']], Q=Q.D, nsim=100)

for(i in 1:length(strees.EV)) strees.EV[[i]] <- reorderSimmap(strees.EV[[i]], "postorder")
for(i in 1:length(strees.D)) strees.D[[i]] <- reorderSimmap(strees.D[[i]], "postorder")

EVtrees.bayou <- lapply(strees.EV, simmap2bayou)
Dtrees.bayou <- lapply(strees.D, simmap2bayou)

```

```{r}
saveRDS(EVtrees.bayou, file="../output/OUwie/EVtrees.rds")
saveRDS(Dtrees.bayou, file="../output/OUwie/Dtrees.rds")
EVtrees.bayou <- readRDS(file="../output/OUwie/EVtrees.rds")
Dtrees.bayou <- readRDS(file="../output/OUwie/Dtrees.rds")


```


```{r}
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.stepEVD <- list(
  EV = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2), 
  D = list(alpha=1, sig2=1, 
                 beta_left=step.starts[2], 
                 beta_right=step.starts[3],
                 beta_center=step.starts[1],
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)

plot(midbins, startpar.stepEVD$EV$theta)


sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoidEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_left=sigmoid.starts$beta_left, 
                 beta_right=sigmoid.starts$beta_right,
                 beta_center=sigmoid.starts$beta_center,
                 beta_slope = sigmoid.starts$beta_slope,
                 k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
                 sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.sigmoidEVD$EV$theta)

linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linearEVD <- list(
  EV = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
  D = list(alpha=1, sig2=0.5, 
                 beta_intercept=linear.starts$beta_intercept, 
                 beta_slope=linear.starts$beta_slope,
                 k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, 
                 theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
                 sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.linearEVD$EV$theta)

```

```{r}
## Set priors
EV.fixed <- list(k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
D.fixed <- list(k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
prior.stepEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
  

prior.sigmoidEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_left="dunif",
                                     dbeta_right="dunif",
                                     dbeta_center="dunif",
                                     dbeta_slope="dlnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_left=list(min=-12, max=0),
                                     dbeta_right=list(min=-12, max=0),
                                     dbeta_center=list(min=-100, max=100),
                                     dbeta_slope=list(meanlog=-3, sdlog=1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)

prior.linearEVD <- list(
  EV = make.prior(tdEV$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=EV.fixed),
  D = make.prior(tdD$phy, plot.prior = FALSE, 
                          dists=list(dalpha="dlnorm", dsig2="dhalfcauchy", 
                                     dbeta_intercept="dnorm",
                                     dbeta_slope="dnorm",
                                     dsb="fixed", dk="fixed", 
                                     dtheta="dnull", dloc="fixed"), 
                          param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1), 
                                     dbeta_intercept=list(mean=-6, sd=1.5),
                                     dbeta_slope=list(mean=0, sd=0.1),
                                     dk="fixed",
                                     dsb="fixed", 
                                     dtheta=list(), dloc="fixed"),
                          fixed=D.fixed)
)
```


```{r}
mymcmcEV.step <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$EV, startpar=startpar.stepEVD$EV, new.dir=TRUE, outname="stepThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.step <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$D, startpar=startpar.stepEVD$D, new.dir=TRUE, outname="stepThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

mymcmcEV.sigmoid <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$EV, startpar=startpar.sigmoidEVD$EV, new.dir=TRUE, outname="sigmoidThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.sigmoid <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$D, startpar=startpar.sigmoidEVD$D, new.dir=TRUE, outname="sigmoidThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

mymcmcEV.linear <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$EV, startpar=startpar.linearEVD$EV, new.dir=TRUE, outname="linearThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.linear <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$D, startpar=startpar.linearEVD$D, new.dir=TRUE, outname="linearThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)

```


```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.step$run(100000)
chainEV.step <- mymcmcEV.step$load()
chainEV.step <- set.burnin(chainEV.step, 0.3)
plot(chainEV.step)

strees.bayou <- Dtrees.bayou
mymcmcD.step$run(100000)
chainD.step <- mymcmcD.step$load()
chainD.step <- set.burnin(chainD.step, 0.3)
plot(chainD.step)

```

```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.sigmoid$run(100000)
chainEV.sigmoid <- mymcmcEV.sigmoid$load()
chainEV.sigmoid <- set.burnin(chainEV.sigmoid, 0.3)
plot(chainEV.sigmoid)

strees.bayou <- Dtrees.bayou
mymcmcD.sigmoid$run(100000)
chainD.sigmoid <- mymcmcD.sigmoid$load()
chainD.sigmoid <- set.burnin(chainD.sigmoid, 0.3)
plot(chainD.sigmoid)
```



```{r}
strees.bayou <- EVtrees.bayou
mymcmcEV.linear$run(100000)
chainEV.linear <- mymcmcEV.linear$load()
chainEV.linear <- set.burnin(chainEV.linear, 0.3)
plot(chainEV.linear)

strees.bayou <- Dtrees.bayou
mymcmcD.linear$run(100000)
chainD.linear <- mymcmcD.linear$load()
chainD.linear <- set.burnin(chainD.linear, 0.3)
plot(chainD.linear)
```

```{r}
optimizeModelsOverSimmap <- function(i, strees.bayou=strees.bayou, chain.sigmoid=chain.sigmoid, chain.linear=chain.linear, chain.step=chain.step, cache = cache){
  sb <- strees.bayou[[i]]$sb
  t2 <- strees.bayou[[i]]$t2
  loc <- strees.bayou[[i]]$loc
  k <- length(sb)
  ntheta <- ncat - 1
lik.sigmoid <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.sigmoid0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               beta_slope = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.tanhLin <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               beta_slope = pp[6],
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))

pp.sigmoid0 <- pp.sigmoid[-5]

lik.step <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = pp[5],
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>pp[5], pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}

lik.step0 <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_left = pp[3],
               beta_right = pp[4], 
               beta_center = 0,
               k = k, 
               ntheta=ntheta,
               theta = ifelse(midbins>0, pp[4], pp[3]),
               sb = sb, 
               loc=loc,
               t2=t2)
  model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]

lik.linear <- function(pp){
  pars <- list(alpha = pp[1],
               sig2 = pp[2], 
               beta_intercept = pp[3],
               beta_slope = pp[4], 
               k = k, 
               ntheta=ntheta,
               theta = pp[3] + pp[4]*midbins,
               sb = sb, 
               loc=loc,
               t2=t2)
  model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))

pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))

opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), 
                      upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200), 
                      upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1), 
                      upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12), 
                      upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1), 
                      upper=c(2000, 2000, 0, 1))

opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))

aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics

return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
```


```{r}
require(optimx)
cacheEV <- bayou:::.prepare.ou.univariate(tdEV$phy, tdEV[['lnVs']], SE=0.02, pred=tdEV['binpred'])
cacheD <- bayou:::.prepare.ou.univariate(tdD$phy, tdD[['lnVs']], SE=0.02, pred=tdD['binpred'])
EVopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=EVtrees.bayou, chain.sigmoid = chainEV.sigmoid, chain.step=chainEV.step, chain.linear=chainEV.linear, cache = cacheEV)
Dopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=Dtrees.bayou, chain.sigmoid = chainD.sigmoid, chain.step=chainD.step, chain.linear=chainD.linear, cache = cacheD)

```

```{r}
summarizeAllOpt <- function(opt.allmodels, cutoff=-Inf){
  all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
  all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
  all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
  all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
  all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
  all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
  all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
  all.aics[all.aics < cutoff] <- NA
  boxplot(all.aics)
  all.aics <- as.data.frame(all.aics)
  t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
  hist(all.aics$aic.linear - all.aics$aic.sigmoid)
  tmp <- reshape2::melt(all.aics)
  aicws <- aicw(tmp$value)$w
  aicws <- round(tapply(aicws, tmp$variable, sum),2)
  return(list(sigmoid=all.sigmoid, step=all.step, sigmoid0=all.sigmoid0, step0 = all.step0, linear=all.linear, tanhLin=all.tanhLin, aics=all.aics, aicws=aicws))
}
sumEV <- summarizeAllOpt(EVopt.allmodels)
sumD <- summarizeAllOpt(Dopt.allmodels)

```

```{r}
sumEV$aicws
sumD$aicws
```

```{r}
dAICs <- apply(sumEV$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV", ylim=c(0,50))
cat("Mean Evergreen dAIC values\n")
apply(t(dAICs), 2, mean)

```
```{r}
dAICs <- apply(sumD$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="D")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC",main="D", ylim=c(0,8))
cat("Mean Deciduous dAIC values\n")
apply(t(dAICs), 2, mean)

```


```{r}
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21)
x <- -600:200
abline(v=0)

dum <- lapply(1:nrow(sumEV$sigmoid), function(i) lines(x, sumEV$sigmoid[i, 3] + sumEV$sigmoid[i, 4]/(1 + exp(sumEV$sigmoid[i, 6]*(x-sumEV$sigmoid[i, 5]))), col="green", lty=1))
dum <- lapply(1:nrow(sumEV$sigmoid0), function(i) lines(x, sumEV$sigmoid0[i, 3] + sumEV$sigmoid0[i, 4]/(1 + exp(sumEV$sigmoid0[i, 5]*(x-0))), col="limegreen", lty=1))

#dum <- lapply(1:nrow(sumEV$tanhLin), function(i) lines(x, sumEV$tanhLin[i, 3] + sumEV$tanhLin[i,6]*x + sumEV$tanhLin[i,4]*tanh(x-sumEV$tanhLin[i,5]), col="green", lty=1))

dum <- lapply(1:nrow(sumD$linear), function(i) lines(x, sumD$linear[i,3] + sumD$linear[i,4]*x, col="orange2", lty=1))
dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i, 4]/(1 + exp(sumD$sigmoid0[i, 5]*(x-0))), col="orange", lty=1))


#dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i,4]/(1+exp(sumD$sigmoid0[i,5]*x)), col="tan", lty=1))



```

```{r}
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21, xlab = "Temp (Cx10)", ylab="Log Vessel Diameter")
x <- -400:200
abline(v=0)

dum <- lapply(seq(1, length(chainEV.sigmoid$gen), 1), function(i) lines(x, chainEV.sigmoid$beta_left[i]+chainEV.sigmoid$beta_right[i]/(1 + exp(chainEV.sigmoid$beta_slope[i]*(x-chainEV.sigmoid$beta_center[i]))), col=makeTransparent(rgb(0, 1, 0), alpha=10)))

dum <- lapply(seq(1, length(chainD.linear$gen), 1), function(i) lines(x, chainD.linear$beta_intercept[i]+chainD.linear$beta_slope[i]*x, col=makeTransparent(rgb(0.9, 0.5, 0.2), alpha=10)))

```

